Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

Rahmati, Omid; Naghibi, Seyed Amir; Shahabi, Himan; Bui, Dieu Tien; Pradhan, Biswajeet; Azareh, Ali; Rafiei-Sardooi, Elham; Samani, Aliakbar Nazari; Melesse, Assefa M.

doi:10.1016/j.jhydrol.2018.08.027

Cited by 138 publications

(101 citation statements)

References 104 publications

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“…RF generates thousands of random binary trees to form a forest. Each tree is grown based on a bootstrap sample, using a classification and regression trees (CART) procedure with a random subset of variables selected at each node [26,45]. For each tree grown on a bootstrap sample, the "out-of-bag" (OOB) error rate is calculated using observations left out of the bootstrap sample.…”

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

“…To run the RF model, the user should optimize two priori parameters, the number of trees in the forest (ntree) and the number of variables tested at each node (mtry), to minimize the OOB error and obtain good model performance [44,45].…”

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

“…The BRT model fits different techniques and combines them to improve the performance of a single model [48,49]. Two different algorithms, namely boosting and regression, are used in the model, and the strengths of these algorithms are combined to improve model accuracy and decrease model variance [45,50]. Boosting is one of the most powerful learning methods for improving model accuracy, by iteratively fitting new trees to the residual errors (RE) of the existing tree assemblage [45,51].…”

Section: Boosted Regression Tree Modelmentioning

confidence: 99%

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Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

2019

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This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had good performance for predicting the spatial landslide distribution in the study area. These LSMs could be helpful for establishing mitigation strategies and for land use planning. deforestation, increased urbanization, and an increase in regional precipitation in landslide-prone areas due to climate change [5]. It is essential that both susceptible and stable areas be identified to mitigate property damage, environmental degradation, and loss of life. Consequently, landslide susceptibility assessments, i.e., assessments of the spatial probability of a landslide occurring, are a huge step forward in the comprehensive hazard management of landslides [6,7]. The landslide susceptibility map (LSM) produced by a landslide susceptibility assessment can be a useful tool for authorities with decision-making capabilities.Many methods and techniques have been proposed to evaluate landslide susceptibility. In the past few decades, statistical approaches have become popular in the use of remote sensing (RS) with a geographic information system (GIS). There are many statistical approaches used in landslide susceptibility assessment, including a frequency ratio (FR) [8,9], certainty factor (CF) [10], statistical index (SI) [11,12], as well as weight of evidence (WoE) [7,13,14] and logistic regression (LR) [15,16] approaches.Recently, machine learning techniques have become popular in various fields. Machine learning, a branch of artificial intelligence, uses computer algorithms to analyze and predict information based on learning from training data [17,18]. Due to its robustness and high generalization capability, the use of machine learning has increased in landslide susceptibility analysis. Among the machine learning methods, artificial neural network [19,20], fuzzy logic [21,22], neuro-fuzzy [23], support vector machine [24,25], random forest [26,27], and naïve Bayes tree [17,28] methods have been popularly applied.More recently, ensemble machine l...

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Section: Landslide Susceptibility Analysismentioning

confidence: 99%

Section: Landslide Susceptibility Analysismentioning

confidence: 99%

Section: Boosted Regression Tree Modelmentioning

confidence: 99%

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Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

2019

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“…Their finding show that although MCDM models could predict flood-prone areas, the data mining algorithms had a higher prediction power than MCDMs since MCDMs rely on expert opinion. Arabameri et al [28] applied an EBF model to the generation of flood susceptibility maps and compared the results with FR, TOPSIS, and VIKOR models, concluding that the EBF model performed best.Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”

mentioning

confidence: 99%

“…Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”

mentioning

confidence: 99%

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

et al. 2019

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Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.In recent years, many statistical and probabilistic models have been tested to prepare flood susceptibility maps [9,10]. Geographic Information System (GIS) has been used as an effective tool for spatial analysis and data manipulation due to its ability to handle large amounts of spatial data [11]. Specifically, the combination of statistical and probabilistic models with Remote Sensing (RS) and GIS has been widely used by different researchers [1,12]. Additionally, some scientists and researchers have studied natural disasters, specifically floods and FSM, with the help of RS and GIS, using different models such as Decision-Tree (DT) [6,13], Support Vector Machine (SVM) [14,15], Frequency Ratio (FR) [16,17], Evidential Belief Function (EBF) [18][19][20], EBF-AHP (Analytical Hierarchy Process) [21], Logistic Regression (LR) [22], Shannon's entropy and...

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Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques–A Review

Singh,

Sharma

2024

Deep Learning Techniques for Automation and Industrial Applications

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Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

Cited by 138 publications

References 104 publications

Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques–A Review

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