A5085115937- Flood Risk Assessment and Management
- Landslides and related hazards
- Seismology and Earthquake Studies
- Earthquake Detection and Analysis
- Groundwater and Watershed Analysis
- Geochemistry and Geologic Mapping
- Cryospheric studies and observations
- Hydrology and Watershed Management Studies
- earthquake and tectonic studies
- Hydrology and Drought Analysis
- Disaster Management and Resilience
- Atmospheric chemistry and aerosols
- Remote Sensing and LiDAR Applications
- Atmospheric aerosols and clouds
- Anomaly Detection Techniques and Applications
- Knowledge Management and Technology
- Rangeland and Wildlife Management
- Remote-Sensing Image Classification
- Ecology and Vegetation Dynamics Studies
- Atmospheric and Environmental Gas Dynamics
- Geophysical Methods and Applications
- Rangeland Management and Livestock Ecology
- Soil and Land Suitability Analysis
- Radioactivity and Radon Measurements
- Aeolian processes and effects
University of Tabriz
2024
Islamic Azad University, Tehran
2020-2022
Islamic Azad University, Damghan Branch
2019-2020
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),...
The main purpose of the present study was to mathematically integrate different decision support systems enhance accuracy seismic vulnerability mapping in Sanandaj City, Iran. An earthquake is considered be a catastrophe that poses serious threat human infrastructures at scales. Factors affecting were identified three dimensions; social, environmental, and physical. Our computer-based modeling approach used create hybrid training datasets via fuzzy-multiple criteria analysis (fuzzy-MCDA)...
Determining areas of high groundwater potential is important for exploitation, management, and protection water resources. This study assesses the spatial distribution in Zarrinehroud watershed Kurdistan Province, Iran using combinations five statistical machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index entropy (IOE), evidential belief (EBF) fuzzy art map (FAM). To accomplish this, 1448 well locations area were randomly divided into two data sets training...
Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage people properties. This study proposed a novel modeling approach for spatial prediction flash based on tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing geospatial data. In this...
Rangelands provide important ecosystem services worldwide. The present study was aimed to map rangeland degradation in a critical mountainous of Iran. carried out based on seven years intensive fieldwork and recording 1147 locations with downward trends the quality rangelands. Twelve conditional factors two ensemble algorithms including Probability density-Index entropy (PD-IOE) Frequency ratio-Index (FR-IOE), were used produce trend (RDT) susceptibility maps. results validation showed that...
Radon potential mapping is challenging due to the limited availability of information. In this study, a new modeling process using deep learning models based on convolution neural network (CNN), long short-term memory (LSTM), and recurrent (RNN) presented predict radon in northwestern part Gangwon Province, South Korea. The used data study are two sets dependent variables (measured soil gas concentrations) independent (radon conditioning factors: lithology; distance from lineament; mean...
Artificial-intelligence and machine-learning algorithms are gaining the attention of researchers in field groundwater modelling. This study explored assessed a new approach based on Gini-, entropy- ratio-based classification trees to predict spatial patterns potential mountainous region Iran. To do this, springs inventory was undertaken, 362 were identified area. A set geo-environmental topo-hydrological factors (slope, aspect, elevation, topographic wetness index, distance from fault,...
Earthquake hazards cause changes in landforms, economic losses, and human casualties. Seismic Vulnerability Mapping (SVM) is key information to prevent predict the damage of earthquakes. The purpose this study train compare results Classification Tree Analysis (CTA) learner model with three Gini, Entropy, Ratio split algorithms, Fuzzy ARTMAP (FAM) by development hybrid models for SVM. Conditioning Factors (SVCFs) such as environmental, physical, social were selected using experts' opinions...
Abstract. Despite years of research on natural hazards and efforts to reduce physical psychological damage, earthquake as a disaster is catastrophic. Though, human the main axis in dealing with crisis vulnerability, since space cities encompasses largest population spectrum, managing this considered an essential issue. Accordingly, vulnerability City Sanandaj was defined by environmental, social criteria. In regard, aim modeling, assessing risk MCDA-ANN hybrid model introduced new method for...
Abstract Land subsidence is a worldwide threat. In arid and semiarid land, groundwater depletion the main factor that induce results in environmental damages, with high economic losses. To foresee prevent impact of land necessary to develop accurated maps magnitude evolution subsidences. susceptibility (LSSMs) provide one effective tools manage vulnerable areas, reduce or subsidence. this study, we used new approach improve Decision Stump Classification (DSC) performance combine it machine...
The frequency and intensity of dust storms in Iran has increased significantly recent years. This study identifies the sources using hybrid algorithms – probability density-index entropy (PD-IOE), density-radial basic function neural network (PD-RBFNN), density-self-organizing map (PD-SOM), density-fuzzy ARTMAP (PD-FAM). Hybrid models employed several effective environmental factors: land cover, slope, soil, use, wind speed, geology, temperature, precipitation. A random selection 70% data...
Abstract This study addresses the critical issue of earthquake vulnerability in Mersin, Türkiye, given its susceptibility to seismic threats due factors such as high population density, substandard constructions, narrow roads, and urban congestion. The research employs a comprehensive approach, utilizing multi-criteria evaluation model novel hybrid random forest estimate city's proportionally. Spatial data encompassing physical, population, building quality, accessibility, relief, hazard...