A5035752085- Flood Risk Assessment and Management
- Landslides and related hazards
- Hydrology and Watershed Management Studies
- Fire effects on ecosystems
- Hydrology and Drought Analysis
- Geotechnical Engineering and Analysis
- Hydrological Forecasting Using AI
- Tree Root and Stability Studies
- Hydrology and Sediment Transport Processes
- Soil erosion and sediment transport
- Groundwater and Watershed Analysis
- Dam Engineering and Safety
- Soil and Unsaturated Flow
- Cryospheric studies and observations
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Rock Mechanics and Modeling
- Tropical and Extratropical Cyclones Research
- Geochemistry and Geologic Mapping
- Environmental and Agricultural Sciences
- Soil and Environmental Studies
- Transboundary Water Resource Management
- Karst Systems and Hydrogeology
- Water Quality and Pollution Assessment
- Grouting, Rheology, and Soil Mechanics
- Water resources management and optimization
University of Kurdistan
2011-2024
Sari Agricultural Sciences and Natural Resources University
2019
This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) GIS to map flood susceptibility in a region of Iran. These algorithms have been used for modelling, infrequently. A total 287 locations were randomly categorized into training (70%; 201 floods), validation (30%; 86 floods) datasets modelling process evaluation. The Step-wise Weight Assessment Ratio Analysis...
This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous categorical conditioning factors were chosen based on 201 locations, including topographic wetness index (TWI), river density, stream power (SPI), curvature, distance from river, lithology,...
The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. spatial database consisted eighteen conditioning factors that were selected information gain ratio (IGR) method. model evaluated quantitative statistical criteria, including sensitivity, specificity, accuracy, root mean squared error...
Landslides have multidimensional effects on the socioeconomic as well environmental conditions of impacted areas. The aim this study is spatial prediction landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) base classifier in northern part Pithoragarh district, Uttarakhand, Himalaya, India. To construct database, ten conditioning factors a total 103 locations ratio 70/30 were used....
Floods are some of the most dangerous and frequent natural disasters occurring in northern region Iran. Flooding this area frequently leads to major urban, financial, anthropogenic, environmental impacts. Therefore, development flood susceptibility maps used identify zones catchment is necessary for improved management decision making. The main objective study was evaluate performance an Evidential Belief Function (EBF) model, both as individual model combination with Logistic Regression...
This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area Van Chan district, Yen Bai province (Vietnam) was carried out with help a database area, considering past landslides 12 conditioning factors. The proposed models were...
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly (FA). This combination could result in ANFIS-ICA ANFIS-FA models, which were applied to flood spatial modelling its mapping the Haraz watershed Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness (TWI), lithology,...
In the present study, Rotation Forest ensemble was integrated with different base classifiers to develop hybrid models namely based Support Vector Machines (RFSVM), Artificial Neural Networks (RFANN), Decision Trees (RFDT), and Naïve Bayes (RFNB) for landslide susceptibility modelling. The validity of these evaluated using statistical methods such as Root Mean Square Error (RMSE), Kappa index, accuracy, area under success rate predictive curves (AUC). Part prone Pithoragarh district,...
Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, aim this study is to compare validate support vector machine (SVM) index entropy (IOE) methods for assessment Cameron Highlands area, Malaysia. For purpose, ten conditioning factors observed landslides were detected by data, WorldView-1 SPOT 5 satellite images. A spatial database was generated including a total 92 locations encompassing same...
In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Model Tree (LMT) and Alternate Decision (ADTree). Eight conditioning factors were distinguished as the most important affecting on of Jeong-am area, slope angle, distance to drift, drift density, geology, lineament, lineament use rock-mass rating (RMR) applied modelling. About 24 previously...
A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) Bagging] was introduced for landslide susceptibility mapping in a part Kamyaran city Kurdistan Province, Iran. spatial database generated which includes total 60 locations set conditioning factors tested by the Information Gain Ratio technique. Performance these models evaluated using area under ROC curve (AUROC) statistical index-based...