Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
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Groundwater Prospects Zonation Based on RS and GIS Using Fuzzy Algebra in Khoh River Watershed, Pauri-Garhwal District, Uttarakhand, India Uday Kumar1, Binay Kumar*2, Neha Mallick3 University Department of Geology, Ranchi University, Ranchi. Principal Technical Officer, Geomatics Solutions Development Group, Centre for Development of Advanced Computing (C-DAC), Pune. 1, 3 2
kumaruday10@gmail.com; *2binay@cdac.in; 3neha.mallick05@gmail.com
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Abstract Water, the most important natural resource, forms the core of ecological system. The advent of remote sensing has opened up new avenues in groundwater prospect evaluation, exploration and management. The groundwater prospect evaluation in watershed region has been attempted based on groundwater potential mapping of the area consisting of thematic maps of geology, drainage, lineament and slope using LISS III satellite images. The Khoh watershed region exhibits diverse hydrogeomorphological conditions where the groundwater regime is controlled mainly by topography and geology. Fuzzy logic operation has been applied on various thematic layers giving various membership values with respect to ground water potential. Moderate groundwater prospects dominate in the area with more than 40% of the study area showing moderate to excellent potential. Keywords
use/land cover, lineaments etc. controlling the occurrence and movement of groundwater (Saraf and Choudhuray, 1998). Remote Sensing and GIS has emerged to meet ever increasing demand for more precise and timely information (Rokade, 2003). The concept of integrated remote sensing and GIS has proved to be an efficient tool in integrating urban planning and ground water studies (Krishnamurthy et al., 2000; Saraf et al. 1998 and Khan et al., 2006). Study Area Khoh river watershed falls in the Pauri district of Uttrakhand between latitude 29⁰41'29''N and 29⁰56'06''N and longitude 78⁰29'54''E and 78⁰42'04''E and occupies an area of about 210 km2 in the Survey of India toposheet number 53K/9 and 53K/10 of 1:50,000 scale. The location map is shown in FIG. 1.
Watershed; Remote Sensing; GIS; Ground Water Prospect Zones; Fuzzy Algebra; Data Integration
Introduction Groundwater is an important part of the natural water cycle, present within underground strata. In the hydrological cycle, groundwater occurs when surface water (rainfall) seeps to a greater depth filling the spaces between particles of soil or sediment or the fractures within rock. Groundwater constitutes an important source of water supply for various purposes, such as domestic, industrial and agricultural needs. Groundwater flows very slowly in the subsurface toward points of discharge, including wells, springs, rivers, lakes, and the ocean. Satellite data provides quick and useful baseline information on the parameters like geology, geomorphology, land
FIG. 1 LOCATION MAP OF THE STUDY AREA
Database Both satellite borne remote sensing data viz. Multidate IRS 1D/P6 LISS III, IRS P-6 AWiFS, LandSat ETM+ and LandSat TM data and other published maps and reports constitute the database necessary for the
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Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
interpretation and delineation of various thematic layers in conjunction with secondary or collateral data. Various thematic layers were prepared using the satellite image and the topographic sheets.
eastern part is the Lower Siwalik formation which consists of sandstone–clay intercalation which is of Eocene age. Lower Siwalik is underline by many formations.
Geological Map
The area of Khoh watershed is lithologically very rich as it composes of all types of major rocks found in the lesser Himalayas. The central part of the area consists of Blaini-Krol-Tal of Eocene succession along with tectonically overlying rocks. The stratigraphic succession of the study area is shown in TABLE 1.
The Garhwal Himalayas geographically forms the central part of the Himalayan Orogenic Belt that runs in an arcuate shape for a strike length of about 2400 km with width varying from 230 to 320 km (Kumar Ravindra, 1992). This orogenic belt formed as a result of northward drift of the Indian plate after it split from the Gondwanaland during Eocene around 50 Ma ago. The collision was followed by continued northward convergence of India against Tibet, resulting in crustal shortening accompanied by uplift and thrusting activity, which led to the thrusting of Higher Himalayan Crystallines over the sediments of the Lesser Himalaya along the Main Central Thrust (MCT). Continued tectonic activity also produced nappes (e.g. Krol) in the Lesser Himalaya which was pushed due south along the Main Boundary Thrust (MBT) and Himalayan Frontal Thrust (HFT).
TABLE 1 STRATIGRAPHIC SUCCESSION OF THE STUDY AREA (AFTER GSI.)
Formation
Lithology
Age
Alluvium & other recent deposits
Sandstone & clay intercalation
Quaternary
Lower Siwalik
Sandstone & clay intercalation
Miocene
Krol thrust Subhatu formation Tal Krol Infrakrol & Blaini Binj Lansdown granite Bijni Amri
Shale, marl and limestone Quartzite with sandstone, phyllite & limestone Limestone, dolomite, shale and slate a) shale, limestone, slate & boulder bed b) boulder, shale sequence with sandy limestone, slate Shale, limestone with subordinate shale
Eocene Cambrian
Upper Proterozoic
Granite-gneiss Bijni thrust Quartzite Amri thrust Phyllite, schist
Structural and Lineament Map
FIG. 2 GEOLOGICAL MAP OF THE STUDY AREA
The rocks exposed in the watershed belong to different geological formations. It is a well-established fact that geological set-up of an area plays a vital role in the distribution and occurrence of groundwater (Krishnamurthy and Srinivas, 1995). The major formations are Amri, Bijni, Binj, Krol, Infrakrol, Blaini, Tal, Subhatu formations as shown in the FIG. 2. Recent deposits of alluvium is found in the south-western part of the study area which dates back to quaternary era. Just adjacent to these deposits towards the south-
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The watershed has many faults with some prominent thrusts also. There are three big thrust just in the middle of the area, all of which form the boundary of the formation as well as lithology. There is a prominent fault in the area which forms the boundary between two formations. Some small faults are found in the eastern part of the area. The main structures found in the area are lineaments, faults and thrust as shown in the FIG. 3. Lineament is a simple or composite linear feature of surface. They are hydrologically very important and may provide the pathway for groundwater movement (Sankar, 2002). Presence of lineaments may act as a conduit for groundwater movement which results in increased secondary porosity and therefore it can serve as groundwater prospective zones (Obi Reddy et al.,
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2000). The extension of large lineaments representing a fault can extend subsurface from hilly terrain to alluvial terrain. It may be a productive groundwater reserve. Similarly, intersection of lineaments can also be probable site of groundwater accumulation.
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they were interpreted as highly, moderately and poorly dissected. Different geological formations developing a variety of land forms such as structural hill, pediments, buried pediments, valley fills etc. have got different capacity of water holding thereby showing varied aquifer qualities (Kumar et. al., 2010a; Kumar et al., 2001 and Kumar et. al., 2010b). Upper, lower and middle slopes of the hills were taken into account from groundwater point of view. Landuse/Landcover Map Landuse/landcover classes were mapped from the satellite images and other existing information by screen visual analysis (FIG. 5). Prominent features include forest (dense and open), agricultural land (on river terrace and hill slope), built-up areas, and riverbed. The repetitive coverage of satellite data plays a vital role in this aspect by depicting the status of watershed over a period of time, indicating the change in the Landuse/Landcover (Kumar et. al., 2012a ; Kumar et. al., 2012b). Care was taken to correlate these features in the geomorphology map and other related themed as some of these features are interrelated.
FIG. 3 STRUCTURAL MAP OF THE STUDY AREA
Geomorphological Map
Slope Map Slope is the most important parameter in groundwater studies as infiltration is inversely related to slope. A break in slope generally initiates groundwater percolation. The slope map has been prepared using SRTM digital elevation model (DEM).
FIG. 4 GEOMORPHOLOGICAL MAP OF THE STUDY AREA
The geomorphological map, interpreted from satellite image and by consulting the geological map and toposheet, consists of prominent units such as low, moderate, and high dissected hills, river terrace, intermontane valley, alluvial fan and piedmont zone as in FIG. 4. Based on drainage density over the hills,
FIG. 5 LANDUSE- LANDCOVER MAP OF THE STUDY AREA
In the study area, slope map (FIG. 6) shows that in the
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Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
lower part of the Khoh river watershed, the slope is very gentle to nearly flat (0-16%), while in the upper part of valley, slope ranges from (16-32%). The overall slope in the valley ranges from (0-16%). Rest of the watershed area, falls in slope ranges from 10% to more than 32%. Valleys have higher prospects of groundwater as having lower slope.
potential zones can be done by analysis of remotely sensed data of drainage, geology, geomorphology and lineament characteristics of the terrain in an integrated way (Rao et. al., 2001). Expert Knowledge Inclusion In classical set theory, the membership of a set is defined as true or false, 1 or 0 respectively. Membership of a fuzzy set, however, is expressed on a continuous scale from 1 (full membership) to 0 (full non-membership). Fuzzy membership values must lie in the range (0, 1), but there are no practical constraints on the choice of fuzzy membership values. Values are simply chosen to reflect the degree of membership of a set, based on subjective judgment. The classes of any map can be associated with fuzzy membership values in an attribute table. The presence of the various states or classes of a map might be expressed in terms of fuzzy memberships of different sets, possibly storing them as several fields in the map attribute table. Not only can a single map have more than one fuzzy membership function, but also several different maps can have membership values for the same proposition.
FIG. 6 SLOPE MAP OF THE STUDY AREA
Fuzzy Logic Based Data Integration Geographic Information System (GIS) is an information system designed to work with data referenced by spatial/geographical coordinates. In other words, GIS is both a database system with specific capabilities for spatially referenced data and a set of operations for working with the data. GIS technology integrates common database operations such as uncertainty and statistical analysis with the unique idea and geographic analysis benefits offered by maps. These abilities distinguish GIS from other information systems and make it valuable to a wide range of public and private enterprises for explaining events, predicting outcomes, and planning strategies (ESRI 2001). The preparation of a groundwater prospect map is primarily based on the synthesis of the data acquired on terrain condition. In a broad sense, contributing factors have been analyzed with respect to lithology, structure, geomorphic features, topography, land use pattern, climatic conditions (Kumar et. al., 2012b). The slope aspect, slope gradient, and lithology information were mainly compiled with the help of ancillary information. An effective evaluation of groundwater
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Fuzzy membership values must reflect the relative importance of each map, as well as the relative importance of each class of a single map. The fuzzy memberships are similar to the combined effect of the class scores and the map weights of the index overlay method. Information and value-based membership values were changed suitably for the following parameters according to expert’s opinion and field observations.
In slope map, the more the slope amount is, higher the runoff will be and hence the less amount of percolation there will be and so low groundwater prospects. Very steep slopes (>600) over cliff faces developed over resistant rocks were assigned relatively low angle membership values compared to the low slopes.
Geomorphic features such as river terraces, river bed, intermontane valley and alluvial plains were given high membership values. In the geomorphology map, highly dissected hills were assigned less values compared to more for areas such as river and terrace deposits. Areas close to faults were assigned more values.
The land use parameters such as agricultural
Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
on land river terrace were assigned higher values which were more than the values of scrub land.
In the lithology map, the alluvium was assigned high membership values, where as quartzites were assigned low value due to their lesser permeability. Lineament density map that indicates the presence of weak zones along joints was assigned high membership values as high groundwater prospects are there.
Similarly, high values were also assigned to high drainage density areas and lesser amount of slopes.
Likewise in each theme, each class is given a fuzzy membership value, based on which the coverages are converted into raster maps.
Data Integration When two or more maps with fuzzy membership functions for the same set are present, a variety of operators can be employed to combine the membership values together. Using normal rules of fuzzy algebra can combine various thematic data layers, represented by respective membership values. For example, in a pixel if a particular litho-unit occurs in combination with a thrust/fault, its membership value can be calculated much higher compared to individual membership values of litho-unit or thrust/fault. This is significant as the resultant effect is expected to be “increasive” in our present consideration and it can be calculated by fuzzy algebraic sum (Parveen et. al., 2012). Similarly, if the presence of two or a set of parameters results in “decreasive” effect, it can be calculated by fuzzy algebraic product. Besides this, fuzzy algebra offers various other methods to combine different data sets in groundwater prospect mapping preparation. Five such operators that can be useful for combing exploration data sets, namely the fuzzy AND, fuzzy OR, fuzzy algebraic product (FAP), fuzzy algebraic sum (FAS) and fuzzy gamma operator.The most significant one is the fuzzy gamma operator which was proposed by Zimmermann et. al., (1980) and by sensible choice of ‘gamma’, it produces output values that ensures a flexible compromise between the ‘increasive’ trends of fuzzy algebraic sum and the ‘decreasive’ effects of the fuzzy algebraic product (Bonham-Carter, 1994).
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In the present study, various fuzzy integration rules such as fuzzy AND, fuzzy OR, fuzzy algebraic product, fuzzy algebraic sum and fuzzy gamma operator were applied on an experimental basis. Fuzzy AND This is equivalent to a Boolean AND (logical intersection) operation on classical set values of (1, 0). It is defined as µCombination = MIN (µA, µB, µC,……) where µA is the membership value for map A at a particular location, µB is the value for map B, and so on. Fuzzy memberships must all be with respect to the same proposition. The effect of this rule is to make the output map be controlled by the smallest fuzzy membership value occurring at each location. Like the Boolean AND, fuzzy AND results in a conservative estimate of set membership, with a tendency to produce small values. The AND operation is appropriate where two or more pieces of evidence for a hypothesis must be present together for the hypothesis to be true. Fuzzy OR On the other hand, the fuzzy OR is the like the Boolean OR (logical union) in that the output membership values are controlled by the maximum values of any of the input maps, for any particular location. The fuzzy OR is defined as µCombination = MAX (µA, µB, µC,……) Using this operator, the combined membership value at a location (=suitability for groundwater etc) is limited only by the most suitable of the evidence maps. In using either the fuzzy AND or fuzzy OR, a fuzzy membership of a single piece of evidence (eg. slope, lithology, drainage, etc.) controls the output value that is the maximum or minimum value of any of the input maps. On the other hand, the following operators combine the effects of two or more pieces of evidence in a "blended” result, so that each data source (input maps) has some effect on the output (map produced). Fuzzy Algebraic Product The combined membership function is defined as n µ combination = ∏ µ i i=1 where µi is the fuzzy membership function for the ith map, and i =1, 2…., n maps are to be combined. The combined fuzzy membership values tend to be very
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Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
small with this operator, due to the effect of multiplying several numbers less than 1. The output is always smaller than, or equal to, the smallest contributing membership value, and therefore “decreasive”. Fuzzy algebraic product map (FIG. 7.), generated by using fuzzy showing very low values due to multiplication of values less than 1. Fuzzy Algebraic Sum This operator is opposite to the algebraic product, defined as the formula given below by Bonham-Carter, (1994).
n µ combination = 1- ∏ (1− µ i) i=1 The result is always larger than (or equal to) the largest contributing fuzzy membership value. The effect is therefore “increasive”. Two pieces of evidence that both favour a hypothesis strengthen one another and the combined evidence is more supportive than either piece of evidence taken individually.
and the fuzzy algebraic sum by = (Fuzzy algebraic sum) µ combination = (FAS) γ ∗ (FAP )1-γ
However, during fuzzy algebraic product and fuzzy algebraic sum operations, it was observed that the average fuzzy resultant values were very low and high, respectively. This is because as many as six different maps were integrated and each map represents a value between 0 and 1. As a result, it was not easy to interpret the fuzzy resultant values in terms of water potential. In order to bring a compromise between these two extreme values, fuzzy gamma operator was applied with various gamma values using the formula given by Bonham-Carter, 1994), where γ is a parameter in the range of (0, 1).
The increasive effect of combining several positive pieces of evidence is automatically limited by the maximum value of 1, 0 which can never be exceeded. The fuzzy algebraic sum map is shown in FIG. 8. The fuzzy algebraic product is an algebraic product, whereas the fuzzy algebraic sum is not an algebraic sum mation .
FIG. 8 GROUNDWATER PROSPECT ZONATION BASED ON FUZZY ALGEBRAIC SUM
FIG. 7 GROUNDWATER PROSPECT ZONATION BASED ON FUZZY ALGEBRAIC PRODUCT
Fuzzy Gamma Operator This is defined in terms of the fuzzy algebraic product
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FIG. 9 GROUNDWATER PROSPECT ZONATION BASED ON FUZZY GAMMA OPERATOR
Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
In fuzzy gamma operation when γ is 1, the combination is the same as the fuzzy algebraic sum; and when γ is 0; the combination equals the fuzzy algebraic product. Here we have taken γ = 0.95 as standard the final product of Fuzzy Gamma shown in FIG. 9. Conclusions The Khoh river watershed shows diverse hydrogeomorphological conditions, where the groundwater regime is controlled by variety of parameters, primarily geomorphology, slope, lithology and landuse-landcover. Four zones of groundwater prospect–Good, Moderate to Good, Poor to Moderate and Poor zones have been delineated based on different algorithms. Groundwater prospects zonation based on Fuzzy membership values and various fuzzy integration rules with the help of GIS based approach clearly indicated that combination of alluvium plains, fine drainage density, gentle slope area are the favourable terrain condition having good groundwater potential zones. Structural and denudational hill, coarse drainage density, high slope areas have poor prospects zones. Piedmont area has relatively good zones as compared to the hilly terrain. The Khoh river watershed is spread over the lesser Himalayas, Siwaliks and alluvium (Bhabhar formation). The topography is highly undulating over lesser Himalayas and Siwaliks where it is almost plain in the Bhabhar terrain. The groundwater occurrence and movement is mainly arbited by the topography and geological formation through which it flows. There is no continuous water table over large distances in lesser Himalayas and Siwaliks. The continuity of the water table in this terrain has been observed for small distances. Spring formation along the different permeability zone is a common phenomenon in areas with undulating topography. These springs have been developed for various purposes. In areas with suitable hydrogeological condition, groundwater is developed through hand pump. Bhabhar area forms potential aquifers and groundwater is developed through tube wells, however, the water level is deep. Water samples are collected from springs, hand pump and streams.
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Ahmedabad, and Head, University Department of Geology, Ranchi University, Ranchi for their cooperation and technical assistance in preparation of this paper. REFERENCES
Agarwal, A. K., and Mishra, D. “Evaluation of groundwater potential in the environs of Jhansi city, Uttar Pradesh through hydrogeological assessment by satellite remote sensing technique.” Journal of the Indian Society of Remote Sensing, 20(3), 121-128, 1992. Agarwal, A. K., Mohan, R., and Yadav, S. K. S. “An integrated approach of remote sensing, GIS and geophysical techniques for hydrological studies in Rajpura block, Budaun district, Uttar Pradesh.” Indian Journal of Power and River Valley Development, 1, 35– 40, 2004. Bonham-Carter, and Graeme F. “Geographic Information Systems for Geoscientists, Modelling with GIS.” Chapter 9, 1994 Environmental
Systems
Research
Institute (ESRI).
“ArcGIS.” 2001. Krishnamurthy, J., and Srinivas, G. “Role of geological and geomorphological factors in groundwater exploration: a study using IRS LISS data.” International Journal of Remote Sensing 16(14):2595–2618, 1995. Krishnamurthy, J., Mani, A., Jayaraman, V., and Manivel, M. “Groundwater resources development in hard rock terrain – an approach using remote sensing and GIS techniques.” J Applied Geology 2(3/4): 204–215, 2000. Kumar, Ravindra. “Fundamentals of Historical Geology and Stratigraphy of India.” Wiley Eastern Ltd., New Delhi, 1992. Kumar, Uday, Kumar, Binay, Mallick, Neha and Kumari, Anjali.
“Groundwater
Hydrogeomorphological
Prospect
Evaluation
using
Method
in
Sanjai
Lower
Watershed, Jharkhand.” Ranchi University Journal of Science and Technology, Vol. II, No.2, 29-33., 2012(a). Kumar, Uday, Reshma, Parveen, and Neha, Mallick. “NDVI Signatures in Watershed Monitoring and Evaluation: a
ACKNOWLEDGMENT
Case Study in Jharkhand.” Abs. National Workshop on
The authors extend their sincere thanks to management of Centre for Development of Advanced Computing, Pune, Space Applications Centre (ISRO),
Recent Advances in Geosciences in Bihar and Jharkhand, 68, 2012 (b). Kumar, B. and Kumar, U. “Application of Remote Sensing
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Global Perspectives on Geography (GPG) Volume 1 Issue 3, August 2013
Techniques in identification of Groundwater Potential
remote sensing data in Upper Vaigai river basin, Tamil
Zones in Sanjai River Basin, Bihar.” Proc. 88th Indian
Nadu, India.” J Indian Soc Remote Sensing 30(30):119–
Science Congress, New Delhi, 5, 2001.
129, 2002.
Kumar, B. and Kumar, U. “Integrated approach using RS
Saraf, A. K. and Choudary, P. R. “Integrated remote sensing
and GIS techniques for mapping of ground water
and GIS for ground water exploration and identification
prospects in Lower Sanjai Watershed, Jharkhand.”
of artificial recharge sites.” Int J Remote Sensing
International Journal of Geomatics and Geosciences
19(10):1825–1841, 1998.
(ISSN 0976 – 4380), Volume 1, No 3, 587 – 598, 2010(b).
Saraf, A. K. and Jain, S.K. “Integrated use of remote sensing
Kumar, U. and Kumar, B. “Ground Water Targeting in Hard
and GIS methods for groundwater exploration in parts of
Rock Terrain using Remote Sensing Techniques in Sanjai
Lalitpur District, U.P.” International Conference on
River Watershed, Jharkhand.” Abs. Regional Workshop
Hydrology and Water Resources, New Delhi, 20–22
on Exploration, Development and Management of
December, 1993.
Groundwater in Hard Rocks: Special reference to
Zimmermann, H. J. and Zysno, P. “Latent Connectives in
Jharkhand State, 25-26 March, 2010, C.G.W.B.-Mid
Human Decision Making.” Fuzzy Sets and Systems 4:37-
Eastern Region, Ministry of Water Resources, Govt of
51.
India; 21-23, 2010(b). Khan, M. A., Narain, Pratap
and Moharana, P. C.
“Prospecting Ground Water Resources Using RS - GIS A Case Study From Arid Western Rajasthan Of India.” Journal of the Indian Society of Remote Sensing, Vol. 34, No. 2, 171 -179, 2006. Obi Reddy, G. P., Chandra Mouli, K., Srivastav, S. K., Srinivas, C. V. and Maji, A. K. “Evaluation of groundwater potential zones using remote sensing dataA case study of Gaimukh watershed, Bhandara district, Maharashtra.” J. Indian Soc. Remote sensing 28(1): 19-32, 2000. Parveen, Reshma and Kumar, Uday. “Geomorphometric Characterization of Upper South Koel Basin, Jharkhand A Remote Sensing & GIS Approach” Journal of Water Resource and Protection, Scientific Research (USA), Vol.4, 1042-1050, 2012. Rao, S. N., Chakradar, G. K. J., and Srinivas, V. “Identification of groundwater potential zones using Remote Sensing Techniques in around Guntur Town, Andhra Pradesh, India.” J Indian Soc Remote Sensing 29(1&2), 69, 2001. Rokade, V. M. “Integrated Geological Investigations for Groundwater Potential and water resource management of Sasti watershed, Taluka Rajura of Chandrapur District (MS) using Remote Sensing and GIS.” Published Doctorate thesis, Nagpur University, Nagpur, 1-4, 2003. Sankar, K. “Evaluation of groundwater potential zones using
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Uday Kumar holds a Ph. D. Degree and a Post Graduate Degree in Earth Sciences from Ranchi University, Ranchi, India. He is an eminent scholar and has significantly contributed in earth sciences domain. He is Associate Professor in the University Department of Geology, Ranchi University, Ranchi and has more than 25 years of teaching experience. He has undertaken many research projects in geology and coal related topics. He has good knowledge of remote sensing and GIS and specializes in natural and water resources management etc. He has published and presented a number of research papers in scientific journals and national and international level conference/seminars. More than 8 students have successfully completed their Ph. D. Under his guidance and supervision and a good number of students are currently enrolled for Ph. D. under him. Dr. Kumar is member of many professional bodies. He has reviewed a number of research papers and his major thrust areas are Coal Bed Methane studies, Coal Petrography, Watershed studies, natural and water resources management. Binay Kumar holds a Ph. D. Degree in Watershed Management and a Post Graduate Degree in Earth Sciences from Ranchi University, Ranchi, India and an M. Tech. degree in Remote Sensing from Birla Institute of Technology, Mesra, India. He has been working in geo-informatics field for the last 12 years and is associated with Centre for Development of Advanced Computing, Pune under Ministry of Communications and Information Technology, Govt. of India. He has undertaken many research projects in the remote sensing and GIS field and specializes in natural and water resources management etc, and has published and presented a number of research papers in scientific journals
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and national level conference/seminars, as well as a chapter on Impact of Climate Change and Glacial Lake Outburst Floods (GLOFs) in Sikkim Himalayas published in a book on Climate Change by Sikkim Government. Dr. Kumar is a life member of Indian Society of Remote Sensing and Indian Society of Geomatics and a Fellow of Society of Earth Scientist. He has also reviewed a number of research papers for journals published by Indian Society of Geomatics. His major thrust areas of research are glaciological applications and climate change studies, natural and water resources management, Watershed management etc. Neha Mallick holds a Post Graduate Diploma in Remote
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Sensing and GIS from Indian Institute of Remote Sensing, Dehradun, India and a Post Graduate Degree in Earth Sciences from Ranchi University, Ranchi, India. She is a research scholar and currently pursuing her Ph. D. from Ranchi University, Ranchi, India. She has worked as a Programme Officer in the ENVIS Centre at Environmental Science Engineering Department, Indian School of Mines, Dhanbad, Jharkhand and has also worked as Assistant Professor in the Department of Geology, St. Xavier's College, Ranchi, India. Ms. Mallick has published and presented a number of research papers in scientific journals and national and international level conference/seminars.
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