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

Özdemir, Adnan

doi:10.1016/j.jhydrol.2011.10.010

Cited by 270 publications

(104 citation statements)

References 82 publications

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“…Several statistical methods can also be adopted for groundwater mapping where adequate information on different influencing parameters to groundwater accumulation and movement are available. These include frequency ratio (Davoodi et al 2013), multi-criteria decision evaluation (Murthy and Mamo 2009;Kumar et al 2014), logistic regression model (Ozdemir 2011), weights-of-evidence model (Ozdemir 2011;Pourtaghi and Pourghasemi 2014), random forest model (Rahmati et al 2016Naghibi et al 2016, maximum entropy model (Rahmati et al 2016), boosted regression tree (Naghibi et al 2016;Naghibi and Pourghasemi 2015), classification and regression tree (Naghibi et al 2016), multivariate adaptive regression spline model (Zabihi et al 2016), certainty factor model (Zabihi et al 2016), evidential belief function (Pourghasemi and Beheshtirad 2015;Naghibi and Pourghasemi 2015), and generalized linear model (Naghibi and Pourghasemi 2015). These information are lacking in many third world country hence proper understanding of hydrogeological characteristics for successful exploitation of groundwater in basement areas depend largely on geophysical methods.…”

Section: Introductionmentioning

confidence: 99%

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

et al. 2017

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The geophysical assessment of groundwater in Awa-Ilaporu, near Ago Iwoye southwestern Nigeria was carried out with the aim of delineating probable areas of high groundwater potential. The area falls within the Crystalline Basement Complex of southwestern Nigeria which is predominantly underlain by banded gneiss, granite gneiss and pegmatite. The geophysical investigation involves the very low frequency electromagnetic (VLF-EM) and Vertical Electrical Sounding (VES) methods. The VLF-EM survey was at 10 m interval along eight traverses ranging between 290 and 700 m in length using ABEM WADI VLF-EM unit. The VLF-EM survey was used to delineate areas with conductive/fractured zones. Twenty-three VES surveys were carried out with the use of Campus Ohmega resistivity meter at different location and at locations areas delineated as high conductive areas by VLF-EM survey. The result of VLF-EM survey along its traverse was used in delineating high conductive/fractured zones, it is, however, in agreement with the delineation of the VES survey. The VES results showed 3-4 geoelectric layers inferred as sandy topsoil, sandy clay, clayey and fractured/fresh basement. The combination of these two methods, therefore, helped in resolving the prospecting location for the groundwater yield in the study area.

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Section: Introductionmentioning

confidence: 99%

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

et al. 2017

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“…As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. In the last decade, some researchers have employed several statistical models such as frequency ratio (Oh et al 2011;Manap et al 2012;Pourtaghi and Pourghasemi 2014;Davoodi Moghaddam et al 2015;Naghibi et al 2015), weights-of-evidence (Ozdemir 2011a;Pourtaghi and Pourghasemi 2014), logistic regression (Ozdemir 2011a;Pourtaghi and Pourghasemi 2014), index of entropy , artificial neural network (Lee et al 2012), analytical hierarchy process (Rahmati et al 2014;Razandi et al 2015) and evidential belief function (Pourghasemi and Beheshtirad 2014) models in the groundwater potential mapping.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

Naghibi

Pourghasemi

2015

Water Resour Manage

230

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As demand for fresh groundwater in the worldwide is increasing, delineation of groundwater spring potential zones become an increasingly important tool for implementing a successful groundwater determination, protection, and management programs. Therefore, the objective of current study is to evaluate the capability of three machine learning models such as boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF), and comparison of their performance by bivariate (evidential belief function (EBF)), and multivariate (general linear model (GLM)) statistical methods in the groundwater potential mapping. This study was carried out in the Beheshtabad Watershed, Chaharmahal-e-Bakhtiari Province, Iran. In total, 1425 spring locations were detected in the study area. Seventy percent of the spring locations were used for model training, and 30 % for validation purposes. Fourteen conditioning-factors were considered in this investigation, including slope angle, slope aspect, altitude, plan curvature, profile curvature, slope length (LS), stream power index (SPI), topographic wetness index (TWI), distance from rivers, distance from faults, river density, fault density, lithology, and land use. Using the above conditioning factors and different algorithms, groundwater potential maps were generated, and the results were plotted in ArcGIS 9.3. According to the results of success rate curves (SRC), values of area under the curve (AUC) for the five models vary from 0.692 to 0.975. In contrast, the AUC for prediction rate curves (PRC) ranges from 77.26 to 86.39 %. The CART, BRT, and RF machine learning techniques showed very good performance in groundwater potential mapping with the AUC Water Resour Manage values of 86.39, 86.12, and 86.05 %, respectively. By the way, The GLM and EBF models in comparison by machine learning models showed weaker performance in spring groundwater potential mapping by the AUC values of 77.26, and 67.72 %, respectively. The proposed methods provided rapid, accurate, and cost effective results. Furthermore, the analysis may be transferable to other watersheds with similar topographic and hydro-geological characteristics.

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“…Many studies implemented frequency ratio method (Ozdemir 2011a,Mohammady et al 2012Ozdemir and Altural 2013), other studies described probabilistic model's application (Murthy and Mamo 2009;Oh HJ et al 2011). Logistic regression approach also was a frequently used method (Ozdemir 2011b).…”

Section: Weight Assignmentmentioning

confidence: 99%

Extract relevant features from DEM for groundwater potential mapping

Liu

Yan

Zhai

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Multi-criteria evaluation (MCE) method has been applied much in groundwater potential mapping researches. But when to data scarce areas, it will encounter lots of problems due to limited data. Digital Elevation Model (DEM) is the digital representations of the topography, and has many applications in various fields. Former researches had been approved that much information concerned to groundwater potential mapping (such as geological features, terrain features, hydrology features, etc.) can be extracted from DEM data. This made using DEM data for groundwater potential mapping is feasible. In this research, one of the most widely used and also easy to access data in GIS, DEM data was used to extract information for groundwater potential mapping in batter river basin in Alberta, Canada. First five determining factors for potential ground water mapping were put forward based on previous studies (lineaments and lineament density, drainage networks and its density, topographic wetness index (TWI), relief and convergence Index (CI)). Extraction methods of the five determining factors from DEM were put forward and thematic maps were produced accordingly. Cumulative effects matrix was used for weight assignment, a multi-criteria evaluation process was carried out by ArcGIS software to delineate the potential groundwater map. The final groundwater potential map was divided into five categories, viz., non-potential, poor, moderate, good, and excellent zones. Eventually, the success rate curve was drawn and the area under curve (AUC) was figured out for validation. Validation result showed that the success rate of the model was 79% and approved the method's feasibility. The method afforded a new way for researches on groundwater management in areas suffers from data scarcity, and also broaden the application area of DEM data.

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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

Cited by 270 publications

References 82 publications

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

Extract relevant features from DEM for groundwater potential mapping

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