Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran

Naghibi, Seyed Amir; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat; Rezaei, Ashkan

doi:10.1007/s12145-014-0145-7

Cited by 272 publications

(103 citation statements)

References 35 publications

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“…The obtained cell values were then classified based on the natural break classification scheme (Pourghasemi and Beheshtirad 2014;Naghibi et al 2015) into low, moderate, high, and very high potential groups (Fig. 4a-e) and Table 3).…”

Section: Groundwater Potential Mapping (Gpm)mentioning

confidence: 99%

“…On the other hand, results showed inverse relationship between altitude, distance from rivers, distance from faults, and river density and degree of belief. A growing body of literature determines the relationship between groundwater conditioning factors and potential (Oh et al 2011;Naghibi et al 2015). The result of Ozdemir (2011b) showed that the elevation and sloperelated factors had a negative correlation with groundwater potential, whereas other factors (TWI, river density, and lineament-related factors) show a positive correlation.…”

Section: The Importance Of Variables In Gpms and Their Relationshipmentioning

confidence: 99%

“…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%

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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

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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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Section: Groundwater Potential Mapping (Gpm)mentioning

confidence: 99%

Section: The Importance Of Variables In Gpms and Their Relationshipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

230

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“…At present, universally-applied geological hazard assessment models include subjective inference analysis models, statistical analysis models, deterministic models, pattern recognition models and the like. Then, different scholars use a variety of theories to study landslide susceptibility assessments, including such techniques as artificial neural networks [5][6][7], logistic regression [8][9][10][11], analytic hierarchy processes (AHP) [2,12,13], the information value method [14][15][16], the certainty factor [17][18][19], fuzzy logic [20][21][22] and an index of entropy [23][24][25]. These methods are mainly based on the analysis of the distributions of landslide hazards and the relationship between the influencing factors.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

Zhao

Yao

Mei

et al. 2017

Entropy

104

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Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen'an County, Shan'xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert's knowledge and the opinions of decision-makers.

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“…The use of different tools and modules such as ArcGIS with other software package will facilitate easy analysis which would have been otherwise tedious and time consuming. Frequency ratio model, Shannon's entropy model, multi-criteria decision making model, decision tree algorithms, weights-of-evidence (WOE) model and evidential belief function (EBF) models are some of the procedures applied for aquifer mapping (Naghibi et al 2014;Duan et al 2016;Guru et al 2016;Nejad et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Delineation of Groundwater Potential Zones in River Basins Using Geospatial Tools—an Example from Southern Western Ghats, Kerala, India

et al. 2017

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The remote sensing and geospatial tools have wide applications in water resource assessment studies. Satellite images are increasingly being used in groundwater exploration because of their utility in identifying various geomorphic features. The present study has been carried out in two important river basins-the Ithikkara and the Kallada river basins, of Southern Western Ghats, Kerala, India. The groundwater potential zones are delineated from 14 thematic layers such as drainage density, geomorphology, slope, lithology, soil, land use/land cover, lineament density, topographic wetness index, rainfall distribution, roughness, curvature, dissection index, depth to water level and topographic position index using multi-criteria evaluation method. Integration of the factors has been performed in GIS platform using weighted overlay analysis. Different classes of thematic maps have been assigned weights based on influence on groundwater hydrology (through discussions with the experts in the field), and finally, factor ranks are assigned. The final map indicates the potentiality values of groundwater occurrence in the study area, which was classified into three categories-high, moderate and low. A total of 37.2% of the study area fall in low groundwater potential zone, 42.3% in moderate potential zone and 20.5% of the area in high potential zone. The groundwater potential zones delineated using the method are validated with data of springs and dug wells from field investigations, and it was found that the overlay method using GIS and remote sensing gives 95% accuracy.

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Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran

Cited by 272 publications

References 35 publications

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

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

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

Delineation of Groundwater Potential Zones in River Basins Using Geospatial Tools—an Example from Southern Western Ghats, Kerala, India

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