Spatial Mapping of Groundwater Potential Using Entropy Weighted Linear Aggregate Novel Approach and GIS

A novel approach for demarcation of groundwater potential by integrating entropy information theory and linear weighted aggregation method has been proposed in this study. Altun Kupri Basin in northern Iraq is selected to explain the benefits of the proposed method. Ten groundwater conditioning factors are chosen depending upon literature reviews, local conditions, and data obtainability. These layers are ground surface elevation, slope angle, plan curvature, topographic wetness index, stream power index, aquifer hydraulic head, aquifer hydraulic conductivity, depth to groundwater, total dissolved solid, and typical soil infiltration rate. Assessment of the importance of the factors in defining groundwater potential using entropy theory reveals that aquifer characteristics such as hydraulic head and hydraulic conductivity are important along with topographic factors in delineating groundwater potentiality. The hydraulic head received the highest entropy weight of 0.23, followed by hydraulic conductivity (0.17), elevation (0.14), and topographic wetness index (0.13). The other factors received entropy weights less than 0.1, which mean that they less contribute in groundwater availability in the basin. The groundwater potential index was constructed using a linear weighted aggregation of all the ten factors. Values of the groundwater potential index are classified into five classes using Jenks classification scheme: very low, low, moderate, high, and very high. Map of potential index was validated using relative operating characteristic curve. The area under relative operating characteristic is found to be 0.74, and therefore, the prediction accuracy was good.

[1]  B. Pradhan,et al.  Application of GIS based data driven evidential belief function model to predict groundwater potential zonation , 2014 .

[2]  M. Flörke,et al.  Future long-term changes in global water resources driven by socio-economic and climatic changes , 2007 .

[3]  Jing-nan Sun,et al.  Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. , 2006, Journal of environmental sciences.

[4]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[5]  A. Corsini,et al.  Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy) , 2009 .

[6]  Shamsuddin Shahid,et al.  A GIS-based integration of catastrophe theory and analytical hierarchy process for mapping flood susceptibility: a case study of Teeb area, Southern Iraq , 2016, Environmental Earth Sciences.

[7]  Mohsen Rezaei,et al.  Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran , 2014, Environmental Earth Sciences.

[8]  G. Jenks.  The Data Model Concept in Statistical Mapping , 1967 .

[9]  R. Hirji,et al.  Water and climate change : impacts on groundwater resources and adaptation options , 2010 .

[10]  A. Ozdemir.  GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison , 2011 .

[11]  B. Pradhan,et al.  Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya , 2012, Natural Hazards.

[12]  A. Al-Abadi.  Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy models , 2017, Applied Water Science.

[13]  Hamid Reza Pourghasemi,et al.  Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran , 2015, Earth Science Informatics.

[14]  Zohre Sadat Pourtaghi,et al.  GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran , 2014, Hydrogeology Journal.

[15]  A. Al-Aboodi,et al.  Optimum Rain-Gauges Network Design of Some Cities in Iraq , 2014 .

[16]  Hans Wackernagel,et al.  Multivariate Geostatistics: An Introduction with Applications , 1996 .

[17]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[18]  J. Delhomme.  Kriging in the hydrosciences , 1978 .

[19]  Alaa M. Al-Abadi,et al.  Groundwater Potential Mapping of the Major Aquifer in Northeastern Missan Governorate, South of Iraq by Using Analytical Hierarchy Process and GIS , 2014 .

[20]  S. Reis,et al.  A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics , 2011 .

[21]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[22]  A. Al-Abadi.  Groundwater potential mapping at northeastern Wasit and Missan governorates, Iraq using a data-driven weights of evidence technique in framework of GIS , 2015, Environmental Earth Sciences.

[23]  A. Al-Abadi,et al.  Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model , 2016, Modeling Earth Systems and Environment.

[24]  H. S. Lim,et al.  Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model , 2015, Arabian Journal of Geosciences.

[25]  Saro Lee,et al.  Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model , 2012, Hydrogeology Journal.

[26]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[27]  M. Hazarika,et al.  Groundwater Drought in the Northwestern Districts of Bangladesh , 2010 .

[28]  A. Ozdemir.  Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey) , 2011 .

[29]  B. Pradhan,et al.  A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia , 2013, Arabian Journal of Geosciences.

[30]  H. Pourghasemi,et al.  Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran , 2016 .

[31]  Samo Drobne,et al.  Multi-attribute Decision Analysis in GIS: Weighted Linear Combination and Ordered Weighted Averaging , 2009, Informatica.

[32]  B. Pradhan,et al.  Prediction of groundwater flowing well zone at An-Najif Province, central Iraq using evidential belief functions model and GIS , 2016, Environmental Monitoring and Assessment.

[33]  Saro Lee,et al.  GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea , 2011 .

[34]  Saro Lee,et al.  Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. , 2012, Journal of environmental management.

[35]  G. Ozolins,et al.  WHO guidelines for drinking-water quality. , 1984, WHO chronicle.

[36]  P. Döll,et al.  Groundwater use for irrigation - a global inventory , 2010 .

[37]  Wu Jianhua,et al.  Groundwater Quality Assessment Based on Improved Water Quality Index in Pengyang County, Ningxia, Northwest China , 2010 .