A5087279621- Flood Risk Assessment and Management
- Hydrology and Watershed Management Studies
- Groundwater and Watershed Analysis
- Hydrology and Drought Analysis
- Soil erosion and sediment transport
- Hydrological Forecasting Using AI
- Landslides and related hazards
- Geochemistry and Geologic Mapping
- Hydrology and Sediment Transport Processes
- Fire effects on ecosystems
- Rangeland and Wildlife Management
- Rangeland Management and Livestock Ecology
- Disaster Management and Resilience
- Soil and Land Suitability Analysis
- Tree Root and Stability Studies
- Atmospheric chemistry and aerosols
- Fire Detection and Safety Systems
- Atmospheric aerosols and clouds
- Plant Ecology and Taxonomy Studies
- Ecology and Vegetation Dynamics Studies
- Groundwater flow and contamination studies
- Cryospheric studies and observations
- Seismology and Earthquake Studies
- Aeolian processes and effects
- earthquake and tectonic studies
Tarbiat Modares University
2019-2024
Soil Conservation and Watershed Management Research
2021-2023
Agricultural Research & Education Organization
2021-2023
Watershed
2022
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...
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of Bayes Network (BN), Naïve (NB), Decision Tree (DT), Multivariate Logistic Regression (MLP) machine learning methods for prediction across Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing information from 57 historical fires set nine spatially explicit explanatory variables, namely elevation,...
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);...
Groundwater potential maps are one of the most important tools for management groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) techniques mapping in Dak Lak Province, Vietnam. A suite well yield data twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic...
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%...
We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Subspace (RS, RS-RAF), Rotation (RF, RF-RAF). Modeling validation were done on 111 locations 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. assessed model performance with statistically based indexes, including sensitivity,...
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...
Floods are among the devastating natural disasters that occurred very frequently in arid regions during last decades. Accurate assessment of flood susceptibility mapping is crucial sustainable development. It helps respective authorities to prevent as much possible their irreversible consequences. The Digital Elevation Model (DEM) spatial resolution one most base layer factors for modeling Flood Probability Maps (FPMs). Therefore, main objective this study was assess influence DEMs 12.5 m...
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...
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),...
Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected Bowen catchment, tributary Burdekin Basin, as our area study; region associated with high density gully networks. aimed use semi-automated object-based networks detection process using combination multi-source multi-scale remote sensing ground-based data. An advanced approach was employed by integrating geographic image analysis (GEOBIA) current machine learning...
Preparation of a flood probability map serves as the first step in management program. This research develops for floods resulting from climate change future. Two models Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. optimistic (RCP2.6) pessimistic (RCP8.5) scenarios considered mapping future rainfall. Moreover, to produce occurrence maps, 263 locations past events used dependent variables. The number 13 factors conditioning was taken independent...
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)...
High-accuracy flood susceptibility maps play a crucial role in vulnerability assessment and risk mitigation. This study assesses the potential application of three new ensemble models, which are integrations adaptive neuro-fuzzy inference system (ANFIS), analytic hierarchy process (AHP), certainty factor (CF) weight evidence (WoE). The experimental area is Trotuș River basin Romania. database for present research consisted 12 flood-related factors 172 locations. quality models was evaluated...
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...
Abstract Increased use and increasing demands pose serious threats to rangelands. In this study, we document a pronounced downward trend in rangeland quality the Alborz Mountains Firozkuh County, Iran using analysis of three machine‐learning models (MLMs). A total 1,147 transects were established evaluate trends from field data collected over 7‐year period. Twelve independent conditional factors analyzed for their relationships range through MLMs—Random Forest (RF), classification regression...