Groundwater potential mapping using a novel data-mining ensemble model

Kordestani, Mojtaba; Naghibi, Seyed Amir; Hashemi, Hossein; Ahmadi, Kourosh; Kalantar, Bahareh; Pradhan, Biswajeet

doi:10.1007/s10040-018-1848-5

Cited by 131 publications

(53 citation statements)

References 41 publications

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“…Moreover, RF was benchmarked as a well-known, flexible [23], simple machine learning algorithm [6], and an efficient model [6,33] to compare our discriminate analysis performances with respect to groundwater potential mapping. The ROC further showed that all three models reached values greater than 70%, which confirm the acceptance of results for such modeling [33,39]. Based on this observation, the GPMs were perfectly coinciding with true events (spring's locations); therefore, they are reliable for resource planning and monitoring.…”

Section: Discussionsupporting

confidence: 73%

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Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Kalantar

Al-Najjar

Pradhan

et al. 2019

Water

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Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods-Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance-to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.

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Section: Discussionsupporting

confidence: 73%

“…Overall, 917 springs locations were randomly split into 70% for training and 30% for validation of the models, based on previous studies [26,36,37]. Initially, based on the literature in [20,33,38,39], site conditions, and data availability, 15 GCFs were considered for modeling, namely: i.…”

Section: Data Preparationmentioning

confidence: 99%

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Kalantar

Al-Najjar

Pradhan

et al. 2019

Water

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“…In general, the regions with higher SPI and STI values have higher potential for groundwater occurrence because they have higher water table [29]. River density presents the drainage capacity which is inverse proportionality of the soil infiltration [10,30,31]. Rainfall is one of the most important factors in groundwater potential model because the more precipitation region are likely to have more groundwater potential [9].…”

Section: Data Usedmentioning

confidence: 99%

“…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

103

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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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“…Despite this, the simple random method has been widely applied by researchers [24,35,44,80]. Another disadvantage of the simple random method is that each pixel of the study area, even presence locations, has an equal chance of being selected as an absence sample [51].…”

Section: Rf Marsmentioning

confidence: 99%

An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping

et al. 2019

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Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.

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Groundwater potential mapping using a novel data-mining ensemble model

Cited by 131 publications

References 41 publications

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

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

An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping

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