Floods are some of the most destructive and catastrophic disasters worldwide. Development management plans needs a deep understanding likelihood magnitude future flood events. The purpose this research was to estimate flash susceptibility in Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), quadratic discriminant analysis (QDA). A geospatial database including...

Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where response time drainage basin is short. Identification probable flash flood locations and development accurate susceptibility maps important for proper management region. With this objective, we proposed compared several novel hybrid computational approaches machine learning methods mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging (Bag-CDT); Dagging (Dag-CDT);...

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone flash a crucial step in hazard management. In present study, Kalvan watershed Markazi Province, Iran, was chosen evaluate susceptibility modeling. Thus, detect flood-prone zones this study area, five machine learning (ML) algorithms were tested. These included boosted...

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches common artificial (ANN) support vector machine (SVM) models Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES the area, namely, altitude, slope, aspect, plan curvature, profile drainage density, distance from...

The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 non-flood locations were identified mapped. Twenty flood-risk factors selected to model using several machine learning techniques: conditional inference random forest (CIRF), gradient boosting (GBM), extreme (XGB) their ensembles. investigate (year 2050) effects changing climates land use on risk, a general circulation (GCM) with representative concentration pathways...

This study predicts forest fire susceptibility in Chaloos Rood watershed Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector (SVM), and boosted tree (BRT). The utilizes 14 set of predictors derived from vegetation indices, climatic variables, environmental factors, topographical features. To assess the suitability models estimating variance bias estimation, training dataset obtained Natural Resources Directorate Mazandaran province...

Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic social activities. The city of Saqqez in Iran susceptible flooding due its specific environmental characteristics. Therefore, susceptibility vulnerability mapping are essential for comprehensive management reduce the harmful effects flooding. primary purpose this study combine Analytic Network Process (ANP) decision-making method statistical models Frequency Ratio (FR),...

In the present study, gully erosion susceptibility was evaluated for area of Robat Turk Watershed in Iran. The assessment performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first tree (BFTree). To best our knowledge, KLR CDTree algorithms have been rarely applied to modeling. first step, from 242 locations that were identified, 70% (170 gullies) selected as training dataset, other 30%...

The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change human interventions. Hazard mapping is essential for local policymaking prevention, planning the mitigation actions, also adaptation extremes. This study proposes novel predictive models susceptibility flood erosion. Furthermore, this elaborates on prioritizing existing sub-basins in terms of erosion susceptibility. A comparative analysis generalized linear model (GLM),...

This research was conducted to determine which areas in the Robat Turk watershed Iran are sensitive gully erosion, and define relationship between erosion geo-environmental factors by two data mining techniques, namely, Random Forest (RF) k-Nearest Neighbors (KNN). First, 242 locations we determined field surveys mapped ArcGIS software. Then, twelve gully-related conditioning were selected. Our results showed that, for both RF KNN models, altitude, distance roads, from river had highest...

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid semi-arid climates easily eroded rocks soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective this research is to accurately detect predict areas prone gully erosion. In paper, we couple hybrid models a commonly used base classifier (reduced pruning error tree, REPTree) AdaBoost (AB), bagging (Bag), random subspace...

Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to high demand for fresh groundwater. Therefore, identification groundwater an important tool occurrence, protection, and management purposes. More specifically, in arid semi-arid regions, one most natural resources as it supplies water during drought period. The present research study focused on delineation Saveh City, northern part Markazi Province Iran. mapping was prepared using hybrid deep learning...

The objective of this research is to propose and confirm a new machine learning approach Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), Bagging (Bag) ensembles for potential groundwater mapping assessing role influencing factors. Yasuj-Dena area (Iran) selected as case study. For regard, database was established with 362 springs locations 12 groundwater-influencing factors (slope, aspect, elevation, stream power index (SPI), length slope (LS), topographic wetness (TWI),...

Prediction of the groundwater nitrate concentration is utmost importance for pollution control and water resource management. This research aims to model spatial in Marvdasht watershed, Iran, based on several artificial intelligence methods support vector machine (SVM), Cubist, random forest (RF), Bayesian neural network (Baysia-ANN) learning models. For this purpose, 11 independent variables affecting changes include elevation, slope, plan curvature, profile rainfall, piezometric depth,...

Large damages and losses resulting from floods are widely reported across the globe. Thus, identification of flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing occurrence in Brisbane river catchment Australia (i.e., topographic, water-related, geological, land use factors) were acquired for further processing modeling. In this study, artificial neural networks (ANN), deep learning (DLNN), optimized DLNN using particle swarm...

The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable management. present study considers the use a Bayesian additive regression trees (BART) algorithm as non-parametric classifier using Sentinel-2A data topographic variables to estimate characteristics, namely basal area (m2/ha), stem volume (m3/ha), density (number/ha). These results were compared with those three other popular machine learning (ML) algorithms, such...

Gully erosion is one of the advanced forms water erosion. Identifying effective factors and gully predicting important tools to control manage such phenomenon. The main purpose this study evaluate effect four different resampling algorithms including cross-validation (5-fold 10-fold) bootstrapping (Bootstrap Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), random forest (RF) models in spatial modeling evaluation head-cut Konduran watershed. For purpose,...

Piping erosion is one of the water erosions that cause significant changes in landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping essential. Boosting a classic algorithm has been successfully applied diverse computer vision tasks. Therefore, this work investigated performance Boosted Linear Model (BLM), Regression Tree (BRT), Generalized (Boost GLM), Deep...

Landslides are most catastrophic and frequently occurred across the world. In mountainous areas of globe, recurrent occurrences landslide have caused huge amount economic losses a large number casualties. this research, we attempted to estimate potential impact climate LULC on future susceptibility in Markazi Province Iran. We considered boosted tree (BT), random forest (RF) extremely randomized (ERT) models for assessment Province. The results evaluation criteria showed that ERT model is...