GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches

Arabameri, Alireza; Rezaei, Khalil; Cerdà, Artemi; Lombardo, Luigi; Rodrigo‐Comino, Jesús

doi:10.1016/j.scitotenv.2018.12.115

Cited by 176 publications

(87 citation statements)

References 127 publications

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“…By comparing the results of each classification method and the distribution of training and validation wells on the high and very high groundwater susceptibility classes, it was found that the natural break classification method gave the most accurate distribution. This agrees with the findings by Arabameri et al [32], in that natural break method is a good classifier in susceptibility mapping; and (4), evaluation of the models performances using area under receiver operating characteristics (AUROC) curve, sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), root mean square error (RMSE) and seed cell area index (SCAI) methods.…”

Section: Methodssupporting

confidence: 90%

“…The groundwater inventory database is of a key role in groundwater potentiality mapping. An inventory map is a target variable for any spatial modeling [32]. The well inventory database was prepared after extensive field visit with a hand GPS (global positioning system), and yield data were collected from the Department of Water Resources Management, Iran.…”

Section: Groundwater Inventory Map (Gwim)mentioning

confidence: 99%

“…As a result, 80 wells have recognized in the study area. 56 wells (70%) of this dataset, were randomly selected to produce the GWPM models [32], whereas the remaining 24 (30%) wells were considered for validation of GWPMs [11]. The training and testing wells locations have been mentioned in Figure 1.…”

Section: Groundwater Inventory Map (Gwim)mentioning

confidence: 99%

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Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

et al. 2019

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Groundwater is one of the most important natural resources, as it regulates the earth’s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.

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Section: Methodssupporting

confidence: 90%

Section: Groundwater Inventory Map (Gwim)mentioning

confidence: 99%

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Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

et al. 2019

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“…A variety of GIS-based approaches for GESM have been proposed and they can be classified into three types: multicriteria decision-making (MCDM), statistical modeling, and machine learning (ML) models. MCDM models are based on the knowledge of decision makers to identify, select, and weight conditioning factors [6][7][8][9][10]. These factors are combined to develop a GE model.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

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“…The variance inflation factor (VIF) and Tolerance (TOL) are very commonly used indicators for checking multicollinearity among parameters [90,91]. TOL values less than 0.1 or 0.2 and VIF values greater than 5 or 10 indicate collinearity between the parameters [17,19,86,89,92]. In the present study, the multicollinearity test of gully erosion conditioning factors (GECFs) was done using Equations (10) and (11) in SPSS software:…”

Section: Multicollinearity Assessmentmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.

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GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches

Cited by 176 publications

References 127 publications

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

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