Application of GIS-Based Evidential Belief Function Model to Regional Groundwater Recharge Potential Zones Mapping in Hardrock Geologic Terrain

Mogaji, Kehinde Anthony; Omosuyi, G.O.; Adelusi, A. O.; Lim, H. S.

doi:10.1007/s40710-016-0126-6

Cited by 85 publications

(33 citation statements)

References 51 publications

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“…The altitude map was divided into five classes (<108, 108-287, 287-535, 553-851 and >851 m) ( Figure 3a). The slope angle mainly controls the process of feeding groundwater, infiltration and runoff, as well as the speed of groundwater movement [13]. The slope angle layer is categorized into five classes and includes 0 Figure 3b).…”

Section: Topographic Parametersmentioning

confidence: 99%

“…GIS and RS (remote sensing) methods are very effective in preparing a groundwater potential map (GPM) and are able to improve the accuracy and speed of groundwater studies [10]. The GIS-based GPM was developed using various methods such as frequency ratio (FR) [5], certainty factor (CF) [11], evidential belief function (EBF) [12,13], logistic regression (LR) [14,15], weight of evidence (WOE) [16] and entropy [17]. Data mining algorithms with the recent advances in IT and big data, such as random forest (RF) [18,19], logistic model tree (LMT) [20], DT (decision trees) [2], CART (classification and regression trees) [17], ANN (artificial neural networks) [21], SVM (support vector machines) [22] and an ensemble of metaheuristic algorithms with an ANFIS (adaptive neuro-fuzzy inference system) [23] are used widely in GPM.…”

Section: Introductionmentioning

confidence: 99%

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Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2019

Water

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In the future, groundwater will be the major source of water for agriculture, drinking and food production as a result of global climate change. With increasing population growth, demand for groundwater has increased. Therefore, sustainable groundwater storage management has become a major challenge. This study introduces a new ensemble data mining approach with bivariate statistical models, using FR (frequency ratio), CF (certainty factor), EBF (evidential belief function), RF (random forest) and LMT (logistic model tree) to prepare a groundwater potential map (GPM) for the Booshehr plain. In the first step, 339 wells were chosen and randomly split into two groups with groundwater yields above 11 m3/h. A total of 238 wells (70%) were used for model training, and 101 wells (30%) were used for model validation. Then, 15 effective factors, including topographic and hydrologic factors, were selected for the modeling. The accuracy of the groundwater potential maps was determined using the ROC (receiver operating characteristic) curve and the AUC (area under the curve). The results show that the AUC obtained using the CF-RF, EBF-RF, FR-RF, CF-LMT, EBF-LMT and FR-LMT methods were 0.927, 0.924, 0.917, 0.906, 0.885 and 0.83, respectively. Therefore, it can be inferred that the ensemble of bivariate statistic and data mining models can improve the effectiveness of the methods in developing a groundwater potential map.

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Section: Topographic Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2019

Water

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“…The details of the implementation of these aforementioned multi-steps are as reported in the studies of Gorsevski et al (2012), Malczewski (1999), Feizizadeh et al (2012), Eastman and Jiang (1996) and Mogaji et al (2014). According to one of the studies, the criterion weights (W k ) determinations for the PPCFs were based on the applied knowledge expert weighting index technique where the 0.039 consistency ratio (CR) estimated value indicates a good consistency of the judgments of the expert opinion (Adiat et al 2012;Mogaji et al 2016). The reordered weight (Z ik ) and the order weights (V ik ) are the other vital components determined for the PPCFs Tables 3 and 4 are the typical Z ik computation results for Z ik and V ik based on the applied OWA index theory at each of the criterion observed locations of the PPCFs using the template model in Fig.…”

Section: The Application Of Owa-drastic Index Model Technique To Groumentioning

confidence: 99%

Application of vulnerability modeling techniques in groundwater resources management: a comparative study

Mogaji

2018

Appl Water Sci

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The sustainability management of groundwater resource globally is challenged by its vulnerability to pollution resulting from anthropogenic activities. In order to address this problem, the DRASTIC index model (DIM) method among the existing vulnerability modeling techniques is commonly used. OWA-DRASTIC index model (ODIM) technique is another recently developed method for the same task. This study investigated the application of these vulnerability-biased modeling methods in a multi-faceted geologic setting at Perak Province, Malaysia with the view of establishing their efficiencies. The models considered seven pollution potential conditioning factors (PPCFs) obtained from difference data sources. Applying the GIS-based multi-criterial algorithm of these models, the PPCFs were related for developing multi-parameters-based vulnerability index model equations. Groundwater vulnerability to pollution index (GVPI) maps was produced from the synthesized estimated results of the applied multi-parameters-based vulnerability index model equations. The reliability of the produced GVPI maps was established using analyzed groundwater quality data results. The obtained prediction accuracy results for the ODIM-based GVPI map and DIM-based GVPI map are 85.71 and 64.29%, respectively. Besides, the regression coefficient results obtained from the spatially estimate from the DIM and ODIM' vulnerability index's values relationship with the pH and manganese concentrations give 83 and 85% for the ODIM technique and 68 and 63% for the DIM technique, respectively. The overall results indicated that the applied ODIM method in the area is a better alternative to the conventional DIM method. The produced GVPI maps can be useful to regional planners and environmental managers entrusted with the protection of groundwater resource.

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“…The extracted lineaments were converted to measurable quantity by computing the lineament density using equation 3. The lineament density map was generated using line density function [45,46]. The lineament density range was from 0 to 3.5 km per square kilometre.…”

Section: Lineamentmentioning

confidence: 99%

Integrating knowledge-based multi-criteria evaluation techniques with GIS for landfill site selection: A case study using AHP

Fagbohun

Aladejana

2016

Materials and Geoenvironment

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A major challenge in most growing urban areas of developing countries, without a pre-existing land use plan is the sustainable and efficient management of solid wastes. Siting a landfill is a complicated task because of several environmental regulations. This challenge gives birth to the need to develop efficient strategies for the selection of proper waste disposal sites in accordance with all existing environmental regulations. This paper presents a knowledge-based multi-criteria decision analysis using GIS for the selection of suitable landfill site in Ado-Ekiti, Nigeria. In order to identify suitable sites for landfill, seven factors -land use/cover, geology, river, soil, slope, lineament and roads -were taken into consideration. Each factor was classified and ranked based on prior knowledge about the area and existing guidelines. Weights for each factor were determined through pair-wise comparison using Saaty's 9 point scale and AHP. The integration of factors according to their weights using weighted index overlay analysis revealed that 39.23 km 2 within the area was suitable to site a landfill. The resulting suitable area was classified as high suitability covering 6.47 km 2 (16.49%), moderate suitability 25.48 km 2 (64.95%) and low suitability 7.28 km 2 (18.56%) based on their overall weights.Key words: landfill, GIS, AHP, environmental management, spatial planning IzvlečekPomembna naloga v mnogih hitro rastočih mestnih naselbinah v deželah v razvoju, ki nimajo izdelanega plana rabe prostora, je zagotoviti trajnostno in učinko-vito ravnanje s trdnimi odpadki. Izbira lokacije ni lahka spričo zapletene okoljske zakonodaje. Odtod izvira potreba po izdelavi učinkovitih scenarijev izbiranja primernih odlagališč v skladu z vso obstoječo okoljsko regulativo. V tem članku poročamo o na znanju utemeljeni mnogokriterijski analizi, opravljeni v povezavi z geografskim informacijskim sistemom (GIS) za izbiro primerne lokacije odlagališča v nigerijskem mestu Ado--Ekiti. Pri izbiri so upoštevali sedem faktorjev -uporabnost, geološko sestavo, rečno mrežo, vrsto tal, nagib zemljišča, razpokanost kamnine in cestno omrežje. Vsak faktor so razdelili na razrede in ga rangirali glede na poprejšnje poznavanje ozemlja in obstoječe smernice. Posameznim faktorjem so pripisovali uteži s po-parnim primerjanjem ob uporabi Saatyjeve 9 stopenjske lestvice in hierarhične analizne metode (AHP). Z integracijo faktorjev glede na njihove uteži ob uporabi utežne indeksne analize podatkovnih slojev so ugotovili, da je 39,23 km 2 preiskovane površine primerno za lociranje odlagališča. Na tej površini so opredelili z ozirom na vse faktorje za zelo primerno 6,47 km 2 (16,49 %), zmerno primerno 25,48 km 2 (64,95 %) in malo primerno 7,28 km 2 (18,56 %) zemljišča.Ključne besede: odlagališče odpadkov, geografski informacijski sistem, hierarična analizna metoda, okoljska analiza, prostorsko načrtovanje

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Application of GIS-Based Evidential Belief Function Model to Regional Groundwater Recharge Potential Zones Mapping in Hardrock Geologic Terrain

Cited by 85 publications

References 51 publications

Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models

Application of vulnerability modeling techniques in groundwater resources management: a comparative study

Integrating knowledge-based multi-criteria evaluation techniques with GIS for landfill site selection: A case study using AHP

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