Omid Rahmati

TL;DR: In this article, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated analytical hierarchy process (AHP), geographic information system (GIS), and remote sensing (RS) techniques in Kurdistan plain, Iran.

TL;DR: In this paper, the application of random forest (RF) and maximum entropy (ME) models for groundwater potential mapping is investigated at Mehran Region, Iran and the results of the GPMs were quantitatively validated using observed groundwater dataset and the receiver operating characteristic (ROC) method.

TL;DR: This study investigates the analytical hierarchy process (AHP), frequency ratio (FR), and certainty factor (CF) models for groundwater potential mapping using geographical information system (GIS) at Varamin Plain, Tehran province, Iran and finds that the FR model performs better than AHP and CF models.

TL;DR: In this article, the authors investigated the application of the frequency ratio (FR) and weights-of-evidence (WofE) models for flood susceptibility mapping in the Golestan Province, Iran.

TL;DR: The first comprehensive comparison among the performances of ten advanced machine learning techniques (MLTs) including artificial neural networks (ANNs), boosted regression tree (BRT), classification and regression trees (CART), generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS), naive Bayes (NB), quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM) is presented.