Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran

Rahmati, Omid; Melesse, Assefa M.

doi:10.1016/j.scitotenv.2016.06.176

Cited by 107 publications

(38 citation statements)

References 60 publications

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“…In order to create the groundwater pollution occurrence probability map, eight conditioning factors ( Figure 4) were applied based on the literature survey (Adiat et al, 2012;Park et al, 2014;Rahmati and Melesse, 2016;Golkarian et al, 2018). Selected conditioning factors include: topographic wetness index (TWI), distance to river, slope, drainage density, soil type, elevation, landuse, and lithology ( Figure 2).…”

Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

“…TWI (Figure 4a) indicates situation of soil moisture, it is related to groundwater flow pattern (Rahmati and Melesse, 2016;Sajedi-Hosseini et al, 2018) and was calculated by 30 m pixel size digital elevation model (DEM) in SAGA GIS environment. Slope percent (Figure 4c) was another important factor affecting groundwater recharge and potentiality, and was obtained from DEM.…”

Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

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A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Hosseini

Malekian

Choubin

et al. 2018

Science of The Total Environment

Self Cite

272

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This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80 percent were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.

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Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Hosseini

Malekian

Choubin

et al. 2018

Science of The Total Environment

Self Cite

272

View full text Add to dashboard Cite

show abstract

“…Groundwater potential refers to the possibility of groundwater occurrence or the amount of groundwater storage across an area [7,8]. Over the past few years, many efforts have been made to assess the groundwater potential in different regions of the world by different researchers [7,9,10]. In these studies, Geographic Information Systems (GIS) and remote sensing-based approaches have been effectively applied for mapping of groundwater potential.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

107

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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“…A avaliação e modelagem destes recursos têm sido feita com sucesso desde a década de 1970, porém, atualmente, o uso de recursos digitais, conjuntamente à análise geoespacial, tem permitido um melhor tratamento e espacialização de dados (FETTER, 1994;FEITOSA, 2008, OH et al, 2011ABDALLA, 2012;ELBEIH, 2015;OIKONOMIDIS, 2015;RAHMATI et al, 2016).…”

Section: Introductionunclassified

“…Existem teorias tradicionais para avaliação da favorabilidade em aquíferos fissurais, como o critério do riacho fenda (lineamentos) e dos aluviões (depósitos de sedimentos), porém estes dependem muito da experiência do responsável pela perfuração do poço tubular, e não tem aplicação na gestão (FEITOSA, 2008). Já o desenvolvimento de métodos computacionais para avaliação da favorabilidade à água subterrânea em aquíferos fissurais contribui com uma avaliação mais precisa e detalhada sobre a área de interesse permitindo eficiência na explotação deste recurso (SMERDON et al, 2009;SOUZA FILHO et al, 2010;ABDALLA, 2012;ELBEIH, 2015;RAHMATI et al, 2016).…”

Section: Introductionunclassified

Uso de geotecnologias na avaliação da favorabilidade hidrogeológica em aquíferos fraturados

2017

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Resumo: Este artigo trata do mapeamento da favorabilidade hidrogeológica em aquíferos fraturados, na região do Circuito das Águas Paulista. As técnicas de sensoriamento remoto e análise geoespacial foram utilizadas para criação e combinação de mapas geológico, fisiográficos, estrutural, de uso e cobertura do solo e de favorabilidade. Estes dados foram manipulados em sistemas de informação geográfica, visando ao mapeamento da favorabilidade hidrogeológica, utilizando lógica Fuzzy. O mapeamento de favorabilidade se mostrou de acordo com os valores de capacidade específica, e com o padrão de distribuição dos poços. Além disso, a superfície de tendência gerada a partir dos dados de capacidade específica se mostrou em conformidade com o mapa de favorabilidade, corroborando a acurácia do método utilizado e demonstrando uma alta favorabilidade hidrogeológica nos municípios e Águas de Lindóia de Lindóia.Palavras Chaves: Lógica Fuzzy. Análise Geoespacial. Potencial hidrogeológico. Circuito das Águas Paulista.Abstract: This article deals with the mapping of hydrogeological favorability in fractured aquifers, in the so called Circuito das Águas Paulista. Remote sensing and geospatial analysis techniques were used to create and combine geological, physiographic, structural, land cover, and favorability maps. These data were manipulated in geographic information systems, aiming the mapping of hydrogeological favorability, using Fuzzy logic. The favorability mapping was shown according to the specific capacity values and to the distribution pattern of the wells. In addition, the trend surface generated from the specific capacity data was in accordance with the favorability map, corroborating the accuracy of the applied method, showing a high hydrogeological favorability in the Águas de Lindóia and Lindóia municipalities.

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Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran

Cited by 107 publications

References 60 publications

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Uso de geotecnologias na avaliação da favorabilidade hidrogeológica em aquíferos fraturados

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