Hourly Groundwater Modelling In Tidal Lowlands Areas Using Extreme Learning Machine-Particle Swarm O

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International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 13, Issue 4 (April 2017), PP.79-87

Hourly Groundwater Modelling In Tidal Lowlands Areas Using Extreme Learning Machine-Particle Swarm Optimization Nurhayati1, Nashrullah Chatib2, Purwoharjono3 1

Department of Civil Engineering, Tanjungpura University, Pontianak, Indonesia 78116. Department of Civil Engineering, Tanjungpura University, Pontianak, Indonesia 78116. 3 Department of Electrical Engineering, Tanjungpura University, Pontianak, Indonesia 78116. 2

ABSTRACT:- The Information groundwater levels are very important in the management of tidal lowland, especially for food crop farming. This study aims to perform modelling groundwater levels using Extreme Learning Machine (ELM) paralleled with the Particle Swarm Optimization (PSO). PSO is used to set the value of the input weights and hidden biases on ELM methods in order to improve the performance of the method ELM. Groundwater levels are modelled is hourly groundwater level at tertiary block. Data input for modelling is the water level in the channel, rainfall and temperature. Results of ground water level predictions using ELMPSO is better than predictions of groundwater levels using ELM. Based on these results, the ELM-PSO can be used in predicting groundwater levels, so as to assist decision makers in determining water management strategies and the determination of appropriate cropping pattern in tidal lowland. Keywords:- extreme learning machine, particle swarm optimization, tidal lowland, groundwater.

I. INTRODUCTION West Kalimantan, Indonesia has an area of lowland approximately 2.94 million hectares, consists of tidal lowlands and non-tidal lowlands. Lowland areas mainly located in coastal areas, in Kubu Raya district, Pontianak district, Bengkayang district, Sambas district, Ketapang district and Singkawang district. Back swamp contained in Kapuas Hulu. Approximately 70% of lowlands in West Kalimantan potential to be developed for food crops, plantations, farms and settlements. Tidal lowland management for food crop farming activities should be supported by the ground water level information. The success of farming in tidal wetlands, especially food crops, should be supported by the management of water [1]. Groundwater levels can be used as indicators of the availability of water for planting pattern [2]. Groundwater levels can be determined by predicting groundwater levels, therefore, necessary modelling can predict groundwater levels are accurate to obtain information on the ground water level tidal lowland land. Modelling for predicting groundwater levels, in research using hybrid between Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO). ELM used in modelling will be improved to increase the performance of ELM using PSO. PSO is used to set the value of the input weights and hidden biases in the methods ELM, in order to improve the performance of the method ELM. PSO based repair ELM is relatively new, since the implementation of the modelling of groundwater flow in tidal lowland land use methods for modelling like this has never been done.The data used in this study is the water level in the channel, rainfall, and air temperature, while the outcome of this study is the height of ground water level in tidal lowland land. The end result of this research can be used to predict the groundwater level in tidal lowland land as the basis for determining water management strategies and the use of farm land, particularly for food crops.

II. EXTREME LEARNING MACHINE (ELM) ELM learning method is applied to minimize the disadvantages of artificial neural networks specifically in case of learning speed. Input weight and hidden bias of ELM are determined randomly, therefore ELM could perform higher learning speed and good generalization performance [3]. According to Huang [4], standard mathematical model for SLFNs with N hidden nodes and g(x) activation

functions

for

N

different

samples

xi , ti 

,

where

xi  xi1 , xi 2 ...,xin T  R n

and

ti  ti1 , ti 2 ...,tim T  R m is expressed by: ~ N

~ N

  g x     g w .x i

i

j

i 1

i

i

j

 bi  o j

(1)

i 1

where: j =1,2,..., N

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