GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

By Seyed Amir Naghibi, Hamid Reza Pourghasemi, Barnali Dixon. Environmental Monitoring & Assessment, 2015

653 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/356f0f314780d9af8f6cc25747d40fcbbda1c2fb

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Citations in the corpus

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, background): “, 2016), and boosted trees for regression and classification (Naghibi et al., 2016).”
Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms (2021, result): “Based on the results of OneR technique, the criteria of altitude, lithology, and drainage density had the most significant impact on the occurrence of groundwater in study area, which is consistent with previous articles [66,67].”
A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China (2017, methodology): “In this study, receiver operating characteristics (ROC) curve was implemented for evaluation of the FFMs produced by LDA, QDA, FR, and WofE models and to compare their results (Oh et al. 2011; Naghibi et al. 2015, 2016; Naghibi and Pourghasemi 2015; Tien Bui et al. 2016).”
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018, neutral): “, 2017), hydrology (Naghibi et al., 2016), epidemiology (Adler et al.”
Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India (2017, methodology): “develop the decision trees in CART (Naghibi et al. 2016).”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, methodology): “The LVQ algorithm is efficiently applied in different fields for landslide susceptibility mapping [82], groundwater potential mapping [83], mineral potential mapping [84], and environmental science [84].”