Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods

Abstract Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities.

[1]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[2]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[3]  Ralph C. Heath,et al.  Basic ground-water hydrology , 1983 .

[4]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[5]  M. Aniya.  Landslide‐Susceptibility Mapping in the Amahata River Basin, Japan , 1985 .

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

[7]  M. Terlien,et al.  Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation , 1997 .

[8]  C. Fergusson,et al.  Dextral transpression in Late Cretaceous continental collision, Sanandaj–Sirjan Zone, western Iran , 2000 .

[9]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[10]  Trevor Hastie,et al.  Generalized linear and generalized additive models in studies of species distributions: setting the scene , 2002 .

[11]  E. Carranza,et al.  Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines , 2003 .

[12]  R. Jaiswal,et al.  Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development--an approach , 2003 .

[13]  P. J. Chilton,et al.  Groundwater: the processes and global significance of aquifer degradation. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[14]  Jerome H Friedman,et al.  Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.

[15]  P. Hamilton,et al.  Groundwater and surface water: A single resource , 2005 .

[16]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[17]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[18]  David Ozonoff,et al.  Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data , 2005, Environmental health : a global access science source.

[19]  Tao Chang,et al.  Application of back-propagation networks in debris flow prediction , 2006 .

[20]  B. Pradhan,et al.  Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia , 2006 .

[21]  Thomas Webster,et al.  Method for mapping population-based case-control studies: an application using generalized additive models , 2006, International journal of health geographics.

[22]  Yong Liu,et al.  Neural network modeling for regional hazard assessment of debris flow in Lake Qionghai Watershed, China , 2006 .

[23]  Richard W. Healy,et al.  Factors influencing ground-water recharge in the eastern United States , 2007 .

[24]  G. Gopinath,et al.  Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India , 2007 .

[25]  Tung-Chiung Chang,et al.  The application of genetic algorithm in debris flows prediction , 2007 .

[26]  Saro Lee,et al.  Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea , 2007 .

[27]  I. Yilmaz.  GIS based susceptibility mapping of karst depression in gypsum: A case study from Sivas basin (Turkey) , 2007 .

[28]  William C. Haneberg,et al.  Landslides: Processes, Prediction, and Land Use , 2007 .

[29]  Diana M. Allen,et al.  Regional evaluation of hydraulic properties in variably fractured rock using a hydrostructural domain approach , 2008 .

[30]  Raul Romero-Calcerrada,et al.  GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain) , 2008, Landscape Ecology.

[31]  N. W. Park,et al.  Quantitative assessment of landslide susceptibility using high‐resolution remote sensing data and a generalized additive model , 2008 .

[32]  Julian D Olden,et al.  Machine Learning Methods Without Tears: A Primer for Ecologists , 2008, The Quarterly Review of Biology.

[33]  G. Madhu,et al.  Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS , 2008 .

[34]  Huimin Liu,et al.  Generalized Additive Model , 2008 .

[35]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[36]  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 .

[37]  Alexei Pozdnoukhov,et al.  Machine Learning for Spatial Environmental Data: Theory, Applications, and Software , 2009 .

[38]  Ercan Kahya,et al.  Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS , 2009, Adv. Eng. Softw..

[39]  Ángel M. Felicísimo,et al.  Modelling the occurrence of gullies in rangelands of southwest Spain , 2009 .

[40]  Cheng-Haw Lee,et al.  GIS for the assessment of the groundwater recharge potential zone , 2009 .

[41]  V. Chowdary,et al.  Integrated remote sensing and GIS‐based approach for assessing groundwater potential in West Medinipur district, West Bengal, India , 2009 .

[42]  D. Fernández,et al.  Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis , 2010 .

[43]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[44]  James Steele,et al.  Geology and lithic procurement in Upper Palaeolithic Europe: a weights-of-evidence based GIS model of lithic resource potential , 2010 .

[45]  B. Muys,et al.  Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests , 2010 .

[46]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[47]  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 .

[48]  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.

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

[50]  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 .

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

[52]  A. Brenning,et al.  Integrating physical and empirical landslide susceptibility models using generalized additive models , 2011 .

[53]  Iuliana Armaş,et al.  Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania , 2012, Natural Hazards.

[54]  Farrokh Nadim,et al.  Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data , 2012, Landslides.

[55]  B. Pradhan,et al.  Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran , 2013, Arabian Journal of Geosciences.

[56]  K. A. N. Adiat,et al.  Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool – A case of predicting potential zones of sustainable groundwater resources , 2012 .

[57]  B. Pradhan,et al.  Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models , 2012 .

[58]  N. Chandrasekar,et al.  Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques , 2012 .

[59]  J. Pereira,et al.  Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .

[60]  Chong Xu,et al.  Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region , 2012, Journal of Earth Science.

[61]  S. Kaliraj,et al.  Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique , 2014, Arabian Journal of Geosciences.

[62]  A. Ozdemir,et al.  A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .

[63]  M. Mohamed,et al.  Relationship between geological structures and groundwater flow and groundwater salinity in Al Jaaw Plain, United Arab Emirates; mapping and analysis by means of remote sensing and GIS , 2014, Arabian Journal of Geosciences.

[64]  B. Pradhan,et al.  Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran , 2014, Environmental Earth Sciences.

[65]  B. Pradhan,et al.  Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran , 2015, Arabian Journal of Geosciences.

[66]  B. Pradhan,et al.  Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya , 2014, Arabian Journal of Geosciences.

[67]  B. Mati,et al.  Identification of groundwater potential zones using remote sensing and GIS in Lake Chala watershed, Kenya , 2013 .

[68]  S. Babu,et al.  Integration of Geology and Geomorphology for Groundwater Assessment using Remote Sensing and GIS Techniques , 2014 .

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

[70]  G. Kazemi,et al.  A review of research dealing with isotope hydrology in Iran and the first Iranian meteoric water line , 2014 .

[71]  Mustafa Neamah Jebur,et al.  Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .

[72]  H. Pourghasemi,et al.  Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS , 2015, Arabian Journal of Geosciences.

[73]  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.

[74]  Omid Rahmati,et al.  Assessment of the Contribution of N-Fertilizers to Nitrate Pollution of Groundwater in Western Iran (Case Study: Ghorveh–Dehgelan Aquifer) , 2015, Water Quality, Exposure and Health.

[75]  R. Tibshirani,et al.  Generalized Additive Models , 1986 .

[76]  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.

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

[78]  T. Glade,et al.  Comparing physically-based and statistical landslide susceptibility model outputs - a case study from Lower Austria , 2015 .

[79]  Hamid Reza Pourghasemi,et al.  Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS , 2015, Earth Science Informatics.

[80]  Seyed Amir Naghibi,et al.  A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping , 2015, Water Resources Management.

[81]  Seyed Amir Naghibi,et al.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran , 2015, Environmental Monitoring and Assessment.

[82]  Arvind Pandey,et al.  Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques , 2015 .

[83]  Hamid Reza Pourghasemi,et al.  Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran , 2015 .

[84]  M. Hashemi,et al.  Investigation the quantitative and qualitative situation of water resources in karstic formations in Azna city. , 2015 .

[85]  Remote sensing and geophysical survey applications for delineating near-surface palaeochannels and shallow aquifer in the United Arab Emirates , 2015 .

[86]  Omid Rahmati,et al.  Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing , 2015, Arabian Journal of Geosciences.

[87]  David C. Wheeler,et al.  Evaluation of the Performance of Smoothing Functions in Generalized Additive Models for Spatial Variation in Disease , 2015, Cancer informatics.

[88]  M. Mohamed,et al.  Groundwater of Abu Dhabi Emirate: a regional assessment by means of remote sensing and geographic information system , 2015, Arabian Journal of Geosciences.

[89]  M. Bahmani,et al.  Ethnobotanical identification of medicinal plants effective on bloat in Lorestan province, West of Iran , 2015 .

[90]  M. Mohamed,et al.  Mapping of tecto-lineaments and investigate their association with earthquakes in Egypt: a hybrid approach using remote sensing data , 2016 .

[91]  Omid Rahmati,et al.  Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models , 2016 .

[92]  H. Pourghasemi,et al.  Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran , 2016 .

[93]  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 .