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Agriculture 5.0: Artificial Intelligence, IoT and Machine Learning 1st Edition Latief Ahmad
Artificial Intelligence and Industrial Applications: Artificial Intelligence Techniques for Cyber-Physical, Digital Twin Systems and Engineering Applications
Environmental Monitoring, IoT and the application of Artificial Intelligence
Studies in Big Data
Volume 143
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.
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Jamal Mabrouki · Mourade Azrour
Editors
Advanced Systems for Environmental Monitoring, IoT and the application of Artificial Intelligence
Editors Jamal Mabrouki
Faculty of Sciences
Mohammed V University
Rabat, Morocco
ISSN 2197-6503
Studies in Big Data
ISBN 978-3-031-50859-2
Mourade Azrour
Faculty of Sciences and Techniques
Computer Sciences Department
Moulay Ismail University of Meknes
Errachidia, Morocco
ISSN 2197-6511 (electronic)
ISBN 978-3-031-50860-8 (eBook) https://doi.org/10.1007/978-3-031-50860-8
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Combining Artificial Intelligence and Systems Thinking Tools to Predict Climate Change ......................................... 1
Vahid Nourani, Hüseyin Gökçeku¸s, Farhad Bolouri, and Jamal Mabrouki
A Mini-Review on Natural and Modified Clays for Removal of Organic and Inorganic Pollutants From Wastewater and Their Other Applications ................................................ 15
Marouane El Alouani, Badr Aouan, Rajaa Bassam, Mohamad EL Dhaybi, Selin Aslan, Asya Akyüz, Jamal Mabrouki, and Hamid Saufi
How Can the Internet of Things (IoT) Be Used to Improve Port Performance—Moroccan Ports Case Study 43 Hajar Raji
Contribution to the Bryoflora of Morocco: Toubkal National Park (TNP) Rhérhaya Valley 59
A. Fakihani, A. Ouhammou, M. Loudiki, and Allal Douira
Innovation and the Sustainable Development in the Arganeraie Biosphere Reserve (ABR) .......................................... 75
Salma El Ghiouan and Said Boujrouf
AI and Smart Technologies for Smart Agriculture Environment ....... 95 Aman Parashar, Jamal Mabrouki, and Jaidev Sharma
Diversity of Endomycorrhizal Fungi in the Rhizosphere of Fig Trees in the Region of Ifrane (Middle Atlas Region of Northern Morocco) ......................................................... 109
Mouad Ballaoui, Soumaya El Gabardi, Mohamed Chliyeh, Karima Selmaoui, Najia Saidi, Moulay Abdelaziz El Alaoui, Najoua Mouden, Amina Rachid Benkirane, Amina Ouazzani Touhami, and Allal Douira
Efficacy of the Combined Application of Based Trichoderma Asperellum Products and Tolclofos-Methyl to Control Rhizoctonia Solani Black Crown Rot in Strawberry .............................. 123
Errifi Azeddine, Berber Fadoua, Ouazzani Chahdi Abdelatif, Najoua Mouden, Kotba Imad, El Kaissoumi Hanane, Karima Selmaoui, Amina Rachid Benkirane, Amina Ouazzani Touhami, and Allal Douira
Variability of Parasitic Specificity in Curvularia Spicifera 145 Kerroum Boutaina, Achajri Nouha, Najoua Mouden, Karima Selmaoui, Amina Rachid Benkirane, Amina Ouazzani Touhami, and Allal Douira
Evaluation of Genetic Intra-Variability and Clonal Selection Within the Main Population Variety (Moroccan Picholine cv) of the Olive Tree (Olea Europaea L.) Grown in Northern Morocco (Ouezzane Region) Using Morphological Descriptors ................. 153
Soumaya El Gabardi, Najoua Mouden, Mohamed Chliyeh, Amina Ouazzani Touhami, Cherkaoui El Modafar, Abdelkarim Filali Maltouf, Ibnsouda Koraichi Saad, Amir Soumia, and Allal Douira
Applied Sciences Green Microalgae for Future Biomass Development 203
Khadija El-Moustaqim, Jamal Mabrouki, and Driss Hmouni
In Vitro and In Vivo Biological Control by Trichoderma Asperellum Against Rhizoctonia Solani a Causal Agent of Collar and Root Rot in Strawberries .................................................... 213
GIS and Remote Sensing-Based Malaria Risk Modeling and Mapping: A Case Study of Dibrugarh District, Assam, India ...... 237
Rani Kumari Shah and Rajesh Kumar Shah
Defluoridation of Youssoufia-Morocco-Mine Fluoride-Contaminated Water by Adsorption on the Fly Ash in Static and Dynamic Reactor 259
Ahmed Moufti, Afaf AMRI, Mohamed Taoufik, Jamal Mabrouki, Mohammed Mountadar, and Hocine Garmes
Study of the Impact of Irrigation with Wastewater Through the Evaluation of Intestinal Parasite Load in the Case of Ouad Rha in Ouazzane, a Northern Moroccan City ........................
Ahmed Chriqui, Yassine Mouniane, Rida Arabi, Issam El-Khadir, Mohammed Benchrifa, Jamal Mabrouki, Ali Keridou, and Driss Hmouni
Social Recommender Systems in E-Learning Environments: A Literature Review
277
289 Houda Oubalahcen and Moulay Driss El Ouadghiri
Bacteriological characterization of dehydrated sludge from the wastewater treatment plant of the city of Kenitra and their impact on the environment ................................
Afaf Sahraoui, Adil Sahraoui, Fatima Zahra Mekaoui, Fatima Oulhcen, and Mohammed Ouhssine
303
Analytic Performance Between 4 and 5G Networks Using Big Data .... 313 Anass Ariss, Imane Ennejjai, Mohammed Benchrifa, Jamal Mabrouki, and Soumia Ziti
Study of the Physicochemical Properties of Lavender Essential Oil (Lavandula Stoechas) ..............................................
Mohammed Benchrifa, Jamal Mabrouki, Anass Ariss, Imane Ennejjai, Driss Hmouni, and Khadija El-Moustaqim
329
Contributors
Ouazzani Chahdi Abdelatif Laboratoire des Productions Végétales, Animales et Agro-Industrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco
Asya Akyüz Little Prince High School-Saint Joseph Fondation, Istanbul, Turkey
Moulay Abdelaziz El Alaoui Laboratory of Plant and Animal Production and Agro-Industry, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
Allal Douira Laboratoire des Productions Végétales, Animales et Agro-Industrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco;
Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, Faculty of Science, University Ibn Tofail, Kenitra, Morocco
Amina Ouazzani Touhami Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, Faculty of Science, University Ibn Tofail, Kenitra, Morocco; Laboratoire des Productions Végétales, Animales et Agro-Industrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco
Afaf AMRI Materials and Processes Department, Team of Innovative and Mechanical Manufacturing Processes, National School of Arts and Crafts, Meknes, Morocco
Badr Aouan Centre des Sciences des Matériaux, Laboratoire de Physico-Chimie des Matériaux Inorganiques et Organiques (LPCMIO), Ecole Normale Supérieure (E.N.S), Mohammed V University in Rabat, Rabat, Morocco
Rida Arabi Laboratory of Natural Resources and Sustainable Development, Faculty of ScienceS, Ibn Tofaïl University—KENITRA-University Campus, Kenitra, Morocco ix
Anass Ariss Department of Computer Science, Faculty of Science, Mohammed V University, Rabat, Morocco
Selin Aslan Little Prince High School-Saint Joseph Fondation, Istanbul, Turkey
Errifi Azeddine Laboratory of Plant and Animal Production and Agro-Industry, Department of Biology, Faculty of Sciences, Ibn Tofaïl University, Kenitra, Morocco
Mouad Ballaoui Laboratory of Plant and Animal Production and Agro-Industry, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
Rajaa Bassam Laboratory of Physical Chemistry of Materials LCPM, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco
Mohammed Benchrifa Laboratory of Spectroscopy, Molecular Modeling Materials, Nanomaterial, Water and Environment, CERNE2D, Faculty of Science, Mohammed V University, Rabat, Agdal, Morocco
Amina Rachid Benkirane Laboratory of Plant and Animal Production and AgroIndustry, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco;
Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, University Ibn Tofail, Kenitra, Morocco; Laboratoire des Productions Végétales, Animales et Agro-Industrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco
Farhad Bolouri Civil Engineering Department, Energy, Environment, and Water Research Center (ENÇESU-2019), Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Turkey
Said Boujrouf Department of Geography, Laboratory of Studies on Resources, Mobility and Attractiveness (LERMA), Faculty of Letters and Human Sciences, Cadi Ayyad University, Marrakech, Morocco
Kerroum Boutaina Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, University Ibn Tofail, Kenitra, Morocco
Mohamed Chliyeh Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, Faculty of Science, University Ibn Tofail, Kenitra, Morocco
Ahmed Chriqui Laboratory of Natural Resources and Sustainable Development, Faculty of ScienceS, Ibn Tofaïl University—KENITRA-University Campus, Kenitra, Morocco
Marouane El Alouani Centre des Sciences des Matériaux, Laboratoire de PhysicoChimie des Matériaux Inorganiques et Organiques (LPCMIO), Ecole Normale Supérieure (E.N.S), Mohammed V University in Rabat, Rabat, Morocco; Little Prince High School-Saint Joseph Fondation, Istanbul, Turkey
Mohamad EL Dhaybi Little Prince High School-Saint Joseph Fondation, Istanbul, Turkey
Salma El Ghiouan Department of Geography, Laboratory of Studies on Resources, Mobility and Attractiveness (LERMA), Faculty of Letters and Human Sciences, Cadi Ayyad University, Marrakech, Morocco
Moulay Driss El Ouadghiri IA Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
Issam El-Khadir Laboratory of Natural Resources and Sustainable Development, Faculty of ScienceS, Ibn Tofaïl University—KENITRA-University Campus, Kenitra, Morocco
Khadija El-Moustaqim Improvement and Valuation of Plant Resources, Faculty of Sciences, Ibn Tofaïl University, KENITRA-University Campus, Kenitra, Morocco
Imane Ennejjai Department of Computer Science, Faculty of Science, Mohammed V University, Rabat, Morocco
Berber Fadoua Laboratoire d’analyse Médicale, CHP Moulay Abdellah Mohammedia, Ministère de La Santé, Rabat, Morocco
A. Fakihani Faculty of Sciences, University Ibn Toufail, Kenitra, Morocco; Faculty of Sciences, Semlalia-University Cadi Ayyad, Marrakech, Morocco
Soumaya El Gabardi Laboratory of Plant and Animal Production and AgroIndustry, Department of Biology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco;
Laboratory of Plant, Animal and Agro-Industry Productions, Ibn Tofail University, Kenitra, Morocco
Hocine Garmes Faculty of Sciences, Chemistry Department, Water and Environment Laboratory, University Chouaib Doukkali, El Jadida, Morocco
Hüseyin Gökçeku¸ s Civil Engineering Department, Energy, Environment, and Water Research Center (ENÇESU-2019), Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Turkey
El Kaissoumi Hanane Laboratoire des Productions Végétales, Animales et AgroIndustrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco
Driss Hmouni Improvement and Valuation of Plant Resources, Faculty of Sciences, Ibn Tofaïl University, KENITRA-University Campus, Kenitra, Morocco; Laboratory of Natural Resources and Sustainable Development, Faculty of ScienceS, Ibn Tofaïl University—KENITRA-University Campus, Kenitra, Morocco
Kotba Imad Laboratoire des Productions Végétales, Animales et Agro-Industrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco
Karima Selmaoui Laboratory of Plant and Animal Production and Agro-Industry, Department of Biology, Faculty of Sciences, Ibn Tofaïl University, Kenitra, Morocco; Laboratoire des Productions Végétales, Animales et Agro-Industrie, Département de Biologie, Faculté Des Sciences, Université Ibn Tofaïl, Kenitra, Morocco
Abdelouahed Kartas Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, Faculty of Science, University Ibn Tofail, Kenitra, Morocco
Ali Keridou Laboratory Al Hayat d’Analyses Médicales d’Ouezzane, Marrakesh, Morocco
M. Loudiki Faculty of Sciences, Semlalia-University Cadi Ayyad, Marrakech, Morocco
Jamal Mabrouki Faculty of Science, Laboratory of Spectroscopy, Molecular Modeling, Materials, Nanomaterial, Water and Environ-ment, CERNE2D, Mohammed V University in Rabat, Rabat, Agdal, Morocco; Laboratory Al Hayat d’Analyses Médicales d’Ouezzane, Marrakesh, Morocco
Abdelkarim Filali Maltouf Microbiology and Molecular Biology Laboratory, Mohammed V University, Rabat, Morocco
Fatima Zahra Mekaoui Natural Resources and Sustainable Development Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco
Soukaina Msairi Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, Faculty of Science, University Ibn Tofail, Kenitra, Morocco
Cherkaoui El Modafar Laboratory of Biotechnology and Molecular Bioengineering Guéliz, Cadi Ayyad University, Marrakech, Morocco
Najoua Mouden LaboratoiredeChimieMoléculaireEtMoléculesde L’Environnement, Faculté Pluridisciplinaire de Nador, Université Mohammed 1Er Oujda, Oujda, Morocco; Laboratory of Molecular Chemistry and Environmental Molecules, Oujda University, Oujda, Morocco
Ahmed Moufti Physical Science Modeling Team, Regional Center for Education and Training Professions, Casablanca-Settat, Morocco
Yassine Mouniane Laboratory of Natural Resources and Sustainable Development, Faculty of ScienceS, Ibn Tofaïl University—KENITRA-University Campus, Kenitra, Morocco
Mohammed Mountadar Faculty of Sciences, Chemistry Department, Water and Environment Laboratory, University Chouaib Doukkali, El Jadida, Morocco
Achajri Nouha Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, University Ibn Tofail, Kenitra, Morocco
Vahid Nourani Faculty of Civil Engineering, Center of Excellence in Hydroinformatics, University of Tabriz, Tabriz, Iran; Civil Engineering Department, Energy, Environment, and Water Research Center (ENÇESU-2019), Faculty of Civil and Environmental Engineering, Near East University, TRNC, Via Mersin 10, Nicosia, Turkey
Houda Oubalahcen IA Laboratory, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
A. Ouhammou Faculty of Sciences, Semlalia-University Cadi Ayyad, Marrakech, Morocco
Mohammed Ouhssine Natural Resources and Sustainable Development Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco
Fatima Oulhcen Natural Resources and Sustainable Development Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco
Aman Parashar Agronomy, ITM University, Gwalior, India
Hajar Raji Laboratory of Studies and Research in Economics and Management Sciences, Sultan Moulay Slimane University, Higher School of Technology, Beni Mellal, Morocco
Ibnsouda Koraichi Saad Laboratory of Microbial Biotechnology, Sidi Mohamed Ben Abdellah University, Fes, Morocco
Adil Sahraoui Independent Water And Electricity Company, Kenitra, Morocco
Afaf Sahraoui Natural Resources and Sustainable Development Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco
Najia Saidi Laboratory of Plant, Animal and Agro-Industry Productions, Team of Botany, Biotechnology and Plant Protection, Faculty of Science, University Ibn Tofail, Kenitra, Morocco
Hamid Saufi Centre des Sciences des Matériaux, Laboratoire de Physico-Chimie des Matériaux Inorganiques et Organiques (LPCMIO), Ecole Normale Supérieure (E.N.S), Mohammed V University in Rabat, Rabat, Morocco
Rajesh Kumar Shah Department of Zoology, D.H.S.K. College, Dibrugarh, Assam, India
Rani Kumari Shah Department of Geography, Cotton University, Guwahati, Assam, India
Jaidev Sharma Faculty of Agronomy, ITM University, Gwalior, India
Amir Soumia Center for Agrobiotechnology and Bioengineering, Labeled Research Unit (AgroBiotech-URL-CNRST 05), CNRST, Cadi Ayyad University Marrakech, Marrakesh, Morocco
Mohamed Taoufik Physical Science Modeling Team, Regional Center for Education and Training Professions, Casablanca-Settat, Morocco
Soumia Ziti Department of Computer Science, Faculty of Science, Mohammed V University, Rabat, Morocco
Combining Artificial Intelligence and Systems Thinking Tools to Predict Climate Change
Vahid Nourani, Hüseyin Gökçeku¸s, Farhad Bolouri, and Jamal Mabrouki
Abstract According to systemic definitions, climate change is considered a complex system. Therefore, a systematic tool is needed to model it for the future. Artificial intelligence (AI) tools such as artificial neural networks (ANN) can be useful for predicting future situations based on available data. In the meantime, System thinking (ST) sees events not in a linear fashion, but in the form of feedback loops. Therefore, this tool can help to predict the climate change situation in the future, especially since different policies can be introduced as climate action and their effects can be examined. Therefore, the combination of the two methods of AI and ST can be more useful for predicting the future of climate change and the impact of climate actions than using each of them alone, based on the ability of each of them to define policy and predict based on data. In this research, the two methods of AI and ST and the studies that have been done using these two methods to predict climate change and its related parameters and also their strengths and weaknesses were examined. In the conducted investigations, it can be concluded that the combination of two methods is better than each of the methods alone, and with the help of the combination, a more correct decision can be made for the future of climate change and what climate actions should be taken.
Keywords Artificial intelligence · System thinking · Modeling and predicting · Complex systems · Climate change
V. Nourani
Faculty of Civil Engineering, Center of Excellence in Hydroinformatics, University of Tabriz, Tabriz, Iran
V. Nourani · H. Gökçeku¸ s · F. Bolouri (B)
Civil Engineering Department, Energy, Environment, and Water Research Center (ENÇESU-2019), Faculty of Civil and Environmental Engineering, Near East University, TRNC, Via Mersin 10, Nicosia, Turkey
e-mail: farhad.bolouri@neu.edu.tr
J. Mabrouki
Laboratory of Spectroscopy, Molecular Modelling, Materials, Nanomaterial, Water and Environment, Mohammed V University in Rabat, Rabat, Morocco
Climate change modeling offers numerous advantages, notably in predicting its repercussions on various aspects of life. For instance, one study employed a model chain to create hazard scenarios for the North Adriatic Sea coast. This approach yielded insights into the spatiotemporal trends of pertinent hazard metrics like sea temperature, atmospheric pressure, and wave height [29]. Another research effort furnished guidance for modeling endeavors, encompassing scoping, estimation, and reporting of climate change mitigation actions’ effects on population health [22]. Furthermore, modeling proves instrumental in rainfall prediction [16] and assessing climate change’s ramifications on aquaculture [50]. Lastly, it aids in evaluating the efficacy of adaptation strategies in countering climate impacts [4].
Artificial intelligence (AI) and machine learning (ML) have emerged as valuable tools for climate change modeling and prediction. Ongoing projects are dedicated to developing novel AI, ML, and mathematical modeling tools that enhance our understanding of the global ocean symbioses’ structure, functioning, and underlying dynamics in the context of climate change [48]. Furthermore, studies have employed data mining techniques in conjunction with generalized neural networks for weather forecasting and climate scenario prediction [34]. AI-based frameworks have been proposed for the extraction of indicators from remote sensing images and predictive estimation of future climate adaptation-related indicators [56]. Additionally, the utilization of AI and data reduction techniques has been suggested to improve the accuracy of long-term air temperature predictions [17]. In parallel research efforts, various ML and statistical modeling techniques have been put into practice and assessed for the purpose of forecasting CO2 emissions, contributing to climate change mitigation [3].
In a research, Nourani et al. [40] studied the impact of climate change on the spatial and temporal changes of the underground water level using ML models. In another research, Nourani et al. [39] soughted to examine the precision and level of uncertainty associated with downscaled climatic data achieved through the utilization of an artificial neural network (ANN). Tien et al. [63] have tried to improve seasonal climate forecasts by using ML technique. Elkiran et al. [15], investigated climate changes in Northern Cyprus using downscaling models based on AI. Also, Mehrvand et al. [35] climate change scenarios have been investigated using statistical downscaling methods and based on AI-based (ANN and Support vector machines).
Climate change, a multifaceted challenge, necessitates a systems thinking (ST) approach for comprehension and mitigation [46]. To illustrate, leveraging data and systems science involves integrating diverse data sources and constructing simulation models that concurrently account for socio-environmental interdependencies. Utilizing the System of Systems (SoS) theorem can facilitate the exploration of synergies across various disciplines and research concepts, enabling a holistic understanding of climate change concerns [52].
ST diverges from traditional problem-solving methods in its holistic approach. While traditional approaches typically break problems into smaller components for
individual solutions, ST examines how a system’s components interact as a unified whole. It also explores a system’s interactions within a larger SoS context. This approach finds application in fields like engineering, sustainability, and innovation, often alongside methodologies such as design thinking and critical ST to inspire inventive problem-solving. Key principles of ST encompass holistic thinking, nonlinearity, and feedback loops [31, 60, 73].
As previously mentioned, ST proves valuable in unraveling the intricate connections between climate change and its repercussions. For example, applying this methodology to comprehend the effects of climate change on livestock production and formulate effective interventions for this systemic issue involves employing a Causal Loop Diagram (CLD). This CLD offers a holistic view of climate change impacts and aids in pinpointing suitable intervention strategies [69]. In addressing the influence of climate change on biodiversity and ecosystem services, the Conservation Standards Framework (CSF), rooted in ST and adaptive management, emerges as a potent tool. It encompasses a comprehensive analysis of climate change’s potential ramifications on species, ecosystems, and ecosystem services, amalgamated with traditional, non-climate threats. This knowledge integration informs project implementation [7]. Furthermore, a ST approach serves as a compass for recognizing the micro-level challenges confronting the agriculture sector and crafting high-level strategies to enhance resilience against disaster-induced and climate change-related adversities. This approach delivers a more comprehensive understanding of disaster and climate change’s impact on agriculture, fostering the argument for integrating climate change adaptation and disaster risk reduction measures into government development planning [24]. As can be seen, ST proves invaluable in comprehending the intricate interplay between climate change and its consequences, enabling the formulation of effective strategies for both mitigation and adaptation.
In this research, in the following sections, first AI and artificial neural networks are examined in detail as one of the most important AI tools, and then ST and principles. Then, some challenges in using AI and similarly ST to model climate change are examined and the proposed solutions are introduced. Finally, the proposal of this research for the solution for AI and ST challenges to model climate change is introduced.
2 Artificial Intelligence
AI can be described as a computational program capable of approximating an agent’s optimal policy in various scenarios, assuming no critical errors in those scenarios. In simpler terms, AI is a program that interprets the world using a chosen language, forecasts future events, and makes optimal decisions [12]. Enhancing the definition of AI involves refining both the world description language and the predictive algorithm [12]. ANN is one of the most widely used tools and models in AI. ANNs belong to a category of computational models inspired by the structure and functioning of the human brain [42]. These networks consist of interconnected nodes, or neurons, which
process and transmit data. ANNs find application in various fields, including image and speech recognition, natural language processing, and predictive modeling. They excel in tasks that prove challenging to solve using conventional algorithms, such as pattern recognition and forecasting. Depending on the specific task, ANNs can undergo training using supervised, unsupervised, or reinforcement learning methods [42].
The key elements in the structure of an ANN include: (a) Neurons and Layers; (b) Weights and Biases; (c) Activation Functions. ANN consist of interconnected nodes, known as neurons, responsible for processing and transmitting information. These neurons are organized into layers, each serving a distinct role within the network. The input layer receives data, the output layer generates the network’s results, and the hidden layers perform intermediate computations. In the network, each neuron is associated with weights and biases that undergo adjustment during training to optimize the network’s performance. Activation functions introduce non-linearity into the network, enabling it to capture complex relationships between inputs and outputs [6, 18, 72].
2.1 Types of Artificial Neural Networks
Various types of ANNs exist, each featuring its unique architecture and applications. Here are some commonly encountered types: (a) Feedforward neural networks: Comprising layers of neurons that process data linearly from input to output, these networks are frequently employed for classification and predictive tasks. (b) Recurrent neural networks: With bidirectional information flow facilitated by feedback connections, they excel in handling sequential data, such as speech recognition and natural language processing. (c) Convolutional neural networks: Tailored for grid-like data structures like images and videos, they employ convolutional layers to extract features and are prevalent in image and video recognition. (d) Self-organizing maps: Utilized in unsupervised learning scenarios like clustering and dimensionality reduction, they employ competitive learning to identify input data patterns. (e) Radial basis function networks: These networks rely on radial basis functions to model input–output relationships, commonly used for function approximation and classification. (f) Hopfield networks: Geared towards associative memory tasks like pattern recognition and image reconstruction, they use feedback connections to store and retrieve patterns from memory [1, 2, 33, 53].
2.2 Artificial Neural Networks Advantages and Disadvantages
Some of the advantages and disadvantages of using a multilayer perceptron neural network are as follows. Some advantages are: (a) Multilayer perceptron neural networks excel at grasping intricate connections between inputs and outputs, making them versatile for various applications; (b) Training options include supervised learning with labeled data or unsupervised learning without labels; (c) They are suitable for both classification and regression tasks. Implementation is relatively straightforward, with the flexibility to use various optimization algorithms. Some disadvantages are: (a) Multilayer perceptron neural networks can be susceptible to overfitting, where the network becomes overly complex and memorizes training data instead of general patterns; (b) Training can be computationally intensive, particularly for large datasets or networks with numerous layers; (c) Effective performance often requires a substantial volume of training data; (d) Sensitivity to hyperparameter choices, such as layer and neuron count and activation function, can impact results [11, 25, 27, 38, 61].
3 System Thinking
ST is a systematic approach employed to comprehend issues and discover solutions. It examines the connections among the components within a system, recognizing their collaboration as a unified entity. Additionally, it evaluates the system’s interactions with other systems within a broader context known as a SoS. This approach finds application in diverse domains, including engineering, sustainability, and innovation. Moreover, it is frequently integrated with other methodologies like design thinking and critical ST to foster the development of inventive problem statements and solutions [5, 14, 32, 71].
Utilizing ST offers several advantages, such as enhanced system performance and reduced errors [49], improved comprehension and control of intricate systems [10], recognition of structural factors influencing decision-making [65], and the capacity to address various facets of system dynamics [26]. Additionally, it aids in pinpointing sector-specific micro-level issues and crafting comprehensive strategies to better address challenges stemming from disasters or climate change-related risks [24]. As previously mentioned, key principles of ST encompass holistic thinking, nonlinearity, and feedback loops.
3.1 Holistic Thinking
Holistic thinking, characterized by its emphasis on the interconnectedness and interdependence of a system’s components [36], is guided by several key principles: (a) Interconnectedness: Acknowledging that everything is interconnected and that the whole surpasses the sum of its parts [9, 62], (b) Wholeness: Viewing the entire system, including its constituent parts, as interconnected and interdependent [9, 62], (c) Context: Taking the operational context of a system into account [54], (d) Complexity: Recognizing the complexity of systems and the necessity to comprehend the relationships between their components [9, 62], (e) Non-linearity: Appreciating that systems are non-linear, with minor changes capable of producing significant impacts on the entire system [62], (f) Emergence: Recognizing that new properties and behaviors can emerge from interactions among a system’s components [58], (g) Feedback loops: Understanding the critical role of feedback loops in systems and their influence on overall system behavior [9, 62], (h) Sustainability: Considering a system’s long-term sustainability, along with its environmental and societal consequences [9, 62].
3.2 Non-Linearity
Non-linearity principles pertain to non-linear systems, where input and output lack a proportional connection. These principles include: (a) Significant Impact of Small Changes: In non-linear systems, minor alterations in one part can profoundly influence the entire system [13], (b) Emergence: Non-linear systems can showcase emergent properties, stemming from component interactions and defying prediction from individual component properties [45], (c) Bifurcation: Non-linear systems may undergo bifurcation, experiencing abrupt behavior shifts due to small parameter changes [45], (d) Self-organization: Non-linear systems can self-organize, autonomously arranging themselves without external intervention [45], (e) Nonproportional Relationships: Non-linear systems lack proportional input–output relationships, making system behavior unscalable by mere input adjustments [57], (f) Feedback Loops: Non-linear systems may feature feedback loops, contributing to unpredictable and unstable system behavior [13], (g) Sensitivity to Initial Conditions: Non-linear systems can be sensitive to initial conditions, resulting in markedly different outcomes due to slight initial variations [13].
3.3 Feedback Loops
Feedback loops, a fundamental concept in ST, involve a process where a system’s output is fed back into the system as input, forming a loop. Key principles related to
feedback loops include: (a) Positive Feedback Loops: These reinforce input, leading to output amplification [21], (b) Negative Feedback Loops: These inhibit input, stabilizing output [21], (c) Interplay of Positive and Negative Feedback Loops: Their interplay can govern robustness in biological networks [21], (d) Weak Coupling Between Intracellular Feedback Loops: Weak coupling can explain clock gene dynamics dissociation [51], (e) Coupled Feedback Loops: They can result in non-genetic heterogeneity in prostate cancer cells [55], (f) Multiple Feedback Loops: Co-existing loops can generate tissue-specific circadian rhythms [44], (g) Emergence: Feedback loops can give rise to emergent properties, emerging from component interactions beyond individual component properties [37].
4 Main Findings and Discussion
While AI and ML offer potential for climate change modeling and prediction, several associated challenges exist. A primary obstacle is the scarcity of complete and accurate data, crucial for training AI models. Several obstacles arise when utilizing AI for climate change modeling, including: (a) Issues with Explainability: AI models frequently exhibit a lack of transparency, posing difficulties in understanding how they arrive at their decisions. This limitation is especially problematic in climate change modeling, where understanding underlying mechanisms is crucial [64], (b) AutoML Limitations: AutoML techniques, primarily designed for computer vision and natural language processing, may not suit climate change applications that frequently involve spatiotemporal data [64], (c) Complex Interdisciplinary Nature: Climate change’s intricate, interdisciplinary nature demands collaboration among experts in various fields, making it difficult to develop AI models that adequately capture system dynamics [74], (d) Data Scarcity and Quality: Insufficient and sometimes subpar climate change data pose difficulties in constructing accurate AI models. Addressing this issue involves using AI-based statistical downscaling models for assessing climate change impacts on hydro-climatological parameters, particularly in water-limited regions [56], (e) Data Obsolescence: AI models may struggle with outdated information post-training, a critical concern in climate change modeling where timely access to accurate, up-to-date data is essential [68]. In sum, AI offers substantial potential in climate change modeling, but overcoming these challenges is crucial to ensure accurate representation of system dynamics and provide valuable decision-making insights.
There are solutions such as transfer learning to overcome some of these challenges. Transfer learning offers a promising avenue for enhancing AI models in climate change prediction by applying knowledge gained in one domain to another. It entails training a model on a source task and then leveraging that knowledge to enhance its performance on a target task. For instance, a deep learning model trained on data from one geographical region can bolster the performance of a model trained on data from a distinct region, a particularly valuable approach in climate change prediction, where data is often scarce and challenging to acquire. Through knowledge
transfer across domains, AI models can undergo more efficient training with reduced data requirements. Furthermore, transfer learning can enhance the interpretability of AI models by shedding light on their predictive mechanisms. In summary, transfer learning holds substantial promise for elevating the performance and interpretability of AI models in climate change prediction [30, 47, 59].
Two significant hurdles when applying ST to model climate change involve the representation of feedback loops and non-linear dynamics, as well as the integration of social, economic, and political variables. Addressing feedback loops and nonlinear dynamics poses a formidable task due to the intricate nature of climate change, which encompasses numerous interconnected elements that can trigger cascading, unpredictable responses [20, 67]. The incorporation of social, economic, and political considerations also presents a formidable challenge since climate change is influenced by a multitude of factors, some of which are region-specific. An accurate assessment of vulnerability to climate change necessitates a comprehensive understanding of these diverse factors [23, 66]. However, feedback loops represent a defining characteristic of ST, manifesting as interactions within the system’s components and stemming from the consequences of internal activities. In a research project utilizing causal loop diagramming to examine the repercussions of climate change on public health in Long Beach, California, researchers identified feedback loops to better grasp how climate change might affect public health in the coming decades [41]. Another study employed feedback loops to compare various climate forcing agents in climate models, offering an alternative approach to the challenges associated with explicit efficacy calculations [28]. Within the realm of tropical forest ecosystems, researchers established a continuous feedback loop as part of a framework for conducting multi-scale integrated analyses of the potential impacts of land use change on both ecological and socio-economic processes within these ecosystems [43]. On the other hand, the cumulative impact of numerous feedback mechanisms can introduce complexity and non-linearity, which can present challenges in validating intricate models and enhancing predictive precision [19, 70]. Nevertheless, the inclusion of feedback loops within models can enhance predictive accuracy by facilitating a more holistic comprehension of the system and its interdependencies [8]. Consequently, despite the hurdles they pose in climate change modeling, feedback loops remain indispensable for comprehending the intricate dynamics of the climate system and enhancing predictive capabilities.
According to the investigations carried out and the limitations and strengths of each of the methods of AI and ST and their tools for predicting climate change and related matters, it is possible to combine these two methods to one model, as two methods that can be complementary, used for modeling and predicting climate changes. In such a way that first the prediction is done using climate data by AI and its tools. Then, ST tools such as the system dynamics, consider the aspects of the work and also examine and create scenarios for different policies, and finally, a more accurate and complete prediction will be made. To show such an action, Fig. 1 can be considered schematically.
As can be seen in Fig. 1, in the proposed method, the two methods of AI and ST work in a complementary way, and the output of each modeling can continue as the
Fig. 1 Schematic diagram of the combination of two methods of AI and ST to predict climate change
input of the other model until an acceptable result is obtained. Climatic data, which is considered the primary and basic input of AI model, is entered into modeling and a prediction is made for one or more parameters that are affected by climate change, such as rainfall, etc. Then, this case is used as an input for ST, and besides that, various cases that affect or are affected by the desired system are also included in ST. Then, with ST tools such as the system dynamics, different scenarios are performed for the cases that are affected or affected by the desired parameter, and finally, the prediction is obtained based on different scenarios. In the meantime, the prediction obtained from the parameter can be defined as a new input for AI and based on that, the prediction can be made again.
5Closure
The study discusses the challenges and potential solutions in using AI and ST for climate change modeling and prediction. It also proposes a complementary approach that combines both AI and ST methods to improve climate change prediction. The use of AI and ML for climate change modeling faces challenges such as data scarcity, computational power requirements, and interpretability issues. Transfer learning is suggested as a solution to enhance AI models by leveraging knowledge from one domain to another, aiding in training and interpretability. The integration of ST into climate change modeling involves addressing feedback loops, non-linear dynamics,
and social, economic, and political variables. While these factors pose challenges, they are essential for understanding climate system dynamics and improving predictions. The proposed approach combines AI and ST methods synergistically. AI initially predicts climate-related parameters based on climatic data, and these predictions become inputs for ST modeling. ST explores various scenarios considering factors influenced by climate change. The process iterates until more accurate predictions are achieved. This integrated approach aims to provide more robust and comprehensive climate change predictions by harnessing the strengths of both AI and ST methodologies. For a comprehensive review of the research related to climate change modeling using AI or ST and their tools, more than 80 studies were reviewed, but it is suggested that for further researches, each of these methods can be done by examining more cases. So that overlapping cases can be identified and even by entering another model into these two methods can be introduced as a new combined model.
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A Mini-Review on Natural and Modified Clays for Removal of Organic and Inorganic Pollutants From Wastewater and Their Other Applications
Marouane El Alouani , Badr Aouan , Rajaa Bassam , Mohamad EL Dhaybi , Selin Aslan, Asya Akyüz, Jamal Mabrouki, and Hamid Saufi
Abstract Natural and modified clays have emerged as promising materials for the removal of organic and inorganic pollutants from wastewater due to their abundance, and environmentally friendly materials, low cost, and high adsorption capacity. This mini-review highlights the effectiveness of natural and modified clays in removing pollutants from wastewater due to their high surface area, pore volume, and ion exchange capacity. Physical and chemical modifications can enhance the adsorption properties of clays, leading to increased adsorption capacity and selectivity towards hazardous compounds in water and wastewater and their other applications. In addition to their use for clays, modified clays are also effective for removing organic like dyes, antibiotics, pesticides, herbicides, and inorganic pollutants such as heavy metals, phosphates, and nitrates from wastewater. This mini-review also describes the use of clay minerals in therapeutic activities and some biological activities. While the use of natural and modified clays for wastewater treatment is promising,
M. El Alouani (B) · B. Aouan · H. Saufi
Centre des Sciences des Matériaux, Laboratoire de Physico-Chimie des Matériaux Inorganiques et Organiques (LPCMIO), Ecole Normale Supérieure (E.N.S), Mohammed V University in Rabat, Rabat, Morocco
e-mail: ma.elalouani@gmail.com
M. El Alouani · M. EL Dhaybi · S. Aslan · A. Akyüz
Little Prince High School-Saint Joseph Fondation, Istanbul, Turkey
R. Bassam
Laboratory of Physical Chemistry of Materials LCPM, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Bd CdtDriss El Harti, B.P.7955, Casablanca, Morocco
J. Mabrouki
Faculty of Science, Laboratory of Spectroscopy, Molecular Modeling, Materials, Nanomaterial, Water and Environ-ment, CERNE2D, Mohammed V University in Rabat, Avenue Ibn Battouta, BP1014 Rabat, Agdal, Morocco
further research is needed to optimize their synthesis and characterization, as well as investigate their long-term performance and environmental impact.
Keywords Natural clays · Modified clays · Wastewater treatment · Organic and inorganic pollutants · Other applications
1 Introduction
Wastewater treatment is a critical process that ensures the protection of public health and the environment [1]. The presence of organic and inorganic pollutants in water and wastewater can have harmful effects, including waterborne diseases and environmental contamination [2].
Physical, chemical, and biological techniques have been used to eliminate organic and inorganic pollutants from wastewater [3], such as chemical precipitation, coagulation, flocculation, membrane separation, biodegradation, oxidation process [4], immobilization [5], and adsorption [6].
Adsorption is becoming increasingly prominent in scientific circles due to its exceptional efficiency, cost-effectiveness, and ease of handling [7]. Moreover, the adsorbents are capable of regeneration via suitable desorption techniques, and there is a wide range of adsorbents available for use. Natural and modified clays have gained significant attention as effective adsorbents for the removal of pollutants from wastewater due to their low cost, abundance, and high adsorption capacity [8].
Several studies have investigated using natural and modified clays to remove various pollutants from wastewater [9, 10]. For instance, natural clays were shown to be effective in removing organic compounds, such as phenol [11], dyes [12], pesticides [8], and other hazardous pollutants such as fluoride [13] from wastewater due to their high adsorption capacity.
Chemical modifications of clays, such as acid and basic activation and organic activation by surfactant materials [8, 14, 15], have been shown to significantly enhance their adsorption properties. For example, acid-activated clay has been used to remove heavy metals, such as Pb(II), Cd(II), and Ni(II) from aqueous solutions [16]. The activation process increases the clay’s surface area and porosity, leading to improved adsorption capacity for organic and inorganic hazardous materials [10]. Other modifications, such as the incorporation of nanoparticles onto the clay surface, have also been investigated for enhancing its adsorption properties [17–19].
In various domains, clay minerals and materials derived from them find extensive applications. They are commonly utilized as dietary supplements and are widely incorporated into pharmaceutical formulations and delivery systems [20], as well as in dermatology practices [21]. Moreover, clay minerals are used in therapies such as pelotherapy or fangotherapy [22]. Additionally, people ingest clay minerals to calm intestinal discomfort and detoxify the body [23].
In summary, natural, and modified clays have shown great potential for the removal of organic and inorganic pollutants from wastewater due to their high adsorption
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