Regional Groundwater Potential Analysis Using Classification and Regression Trees

Choubin, Bahram; Rahmati, Omid; Soleimani, Freidoon; Alilou, Hossein; Moradi, Ehsan; Alamdari, Nasrin

doi:10.1016/b978-0-12-815226-3.00022-3

Cited by 45 publications

(22 citation statements)

References 37 publications

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“…Slope factor has a very significant role in groundwater potential mapping, and therefore a slope percent map of the study area was prepared [29] (Figure 4a). RSP can be applied to identify topographic characteristics such as valleys, ridge tops, flat surfaces, mid-slopes, foot-slopes, and upper slopes, with the value for RSP ranging from 0 (flat surface and valleys) to 1 (upper-slopes and ridge tops) [39]. In this study, a RSP map was prepared using SAGA-GIS ( Figure 4b).…”

Section: Geo-environmental Factorsmentioning

confidence: 99%

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Moghaddam

Rahmati

Haghizadeh

et al. 2020

Water

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In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region.

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Section: Geo-environmental Factorsmentioning

confidence: 99%

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Moghaddam

Rahmati

Haghizadeh

et al. 2020

Water

Self Cite

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“…The effective management of groundwater is critical for balancing different demands on water. Groundwater management is becoming progressively challenging due to rapid population growth and occurrences of intermittent and prolonged drought periods [3][4][5][6] that have mounted increasing pressure on groundwater resources worldwide [7]. Therefore, further research on the exploration and estimation of groundwater potential and resources is necessary for proper water management of an area.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Nguyen¹,

Avand

et al. 2020

Applied Sciences

143

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Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

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“…Because of lack of reasonable and trustworthy techniques, most spatial modeling studies reported in the literature use a simple random method to select absence samples in different fields, for example, groundwater potential [3,5,10,[13][14][15][16], landslide susceptibility [1,[17][18][19][20][21], gully erosion susceptibility [22][23][24][25], land subsidence susceptibility [26][27][28][29][30][31], and flood susceptibility [18,[32][33][34][35][36][37]. However, the random sampling method has some drawbacks.…”

Section: Introductionmentioning

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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Regional Groundwater Potential Analysis Using Classification and Regression Trees

Cited by 45 publications

References 37 publications

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

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

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