A5039122205- Flood Risk Assessment and Management
- Landslides and related hazards
- Hydrology and Watershed Management Studies
- Soil erosion and sediment transport
- Hydrology and Drought Analysis
- Groundwater and Watershed Analysis
- Hydrological Forecasting Using AI
- Fire effects on ecosystems
- Cryospheric studies and observations
- Remote-Sensing Image Classification
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Radioactivity and Radon Measurements
- Geochemistry and Geologic Mapping
- Hydrology and Sediment Transport Processes
- Soil and Unsaturated Flow
- Tropical and Extratropical Cyclones Research
- Soil and Land Suitability Analysis
- Dam Engineering and Safety
- Disaster Management and Resilience
- Geotechnical Engineering and Analysis
- Species Distribution and Climate Change
- Soil Geostatistics and Mapping
- Remote Sensing in Agriculture
- Geophysical Methods and Applications
- Remote Sensing and Land Use
Korea Institute of Geoscience and Mineral Resources
2020-2025
Korea University of Science and Technology
2020-2025
University of Hawaiʻi at Mānoa
2022-2024
University of Hawaii System
2023-2024
Islamic Azad University North Tehran Branch
2015-2020
Islamic Azad University, Science and Research Branch
2014
Identification of flood-prone sites in urban environments is necessary, but there insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping areas. This study evaluated the capability convolutional neural network (NNETC) recurrent (NNETR) mapping. A flood-inundation inventory (including 295 flooded sites) was used as response variable 10 flood-affecting factors were considered...
The most dangerous landslide disasters always cause serious economic losses and human deaths. contribution of this work is to present an integrated modelling framework, in which adaptive neuro-fuzzy inference system (ANFIS) combined with the two optimization algorithms whale algorithm (WOA) grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA GWO are used as meta-heuristic improve prediction performance ANFIS-based methods. In addition, step-wise weight assessment ratio...
In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, our analysis results were used prepare inventory map containing 359 events identified from Google Earth, aerial photographs, other validated sources. A support vector regression (SVR) model was divide into training (70%) testing (30%) datasets. The produced...
The concept of leveraging the predictive capacity predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores different approaches to LS modelling using artificial intelligence. key objective this study is estimate a map Taleghan-Alamut basin Iran Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost CDT-SubSpace) hybrid approaches, which are...
Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and loss, mitigating the negative impacts on ecosystem services, quality, flooding infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance flexibility. However, an understanding potential these provide fast, cheap, accurate predictions lacking. This study provides first quantification this potential. Spatial are made using three...
The present study has been carried out in the Tabriz River basin (5397 km2) north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range 0 150.9 %. average annual minimum maximum temperatures are 2 °C 12 °C, respectively. rainfall ranges 243 641 mm, northern southern parts of receive highest amounts. In this study, we mapped groundwater potential (GWP) with a new hybrid model combining random subspace (RS) multilayer perception (MLP), naïve Bayes tree...
Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and change land-use planning. This work exploring researching the potential a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution spatial mapping susceptibility erosion. The new machine learning approach combine extreme (XGBoost) algorithm (GA). GA metaheuristic being used improve efficiency XGBoost classification...
Flood-susceptibility mapping is an important component of flood risk management to control the effects natural hazards and prevention injury. We used a remote-sensing geographic information system (GIS) platform machine-learning model develop susceptibility map Kangsabati River Basin, India where flash common due monsoon precipitation with short duration high intensity. And in this subtropical region, climate change's impact helps influence distribution rainfall temperature variation. tested...
The study area was the Anseong-si that located in southernmost part of Gyeonggi-do Province at 127°19′ E, 36°82′ N. Anseong has a transitional climate between north and south regions. Its is characterized by geographical conditions forming expansive plains stretch from Charyeong Range. entire city surrounded many high low mountains to west, late-middle age old hills are spread, while there due development rivers. In this study, machine learning algorithms were used based on convolutional...
Abstract. The objective of the current study is to evaluate seismic vulnerability school buildings in Tehran city based on analytic hierarchy process (AHP) and geographical information system (GIS). To this end, peak ground acceleration, slope, soil liquefaction layers were utilized for developing a geotechnical map. Also, construction materials structures, age construction, quality, resonance coefficient defined as major factors affecting structural buildings. Then, AHP method was applied...
Abstract. The main issue in determining seismic vulnerability is having a comprehensive view of all probable damages related to earthquake occurrence. Therefore, taking into account factors such as peak ground acceleration at the time occurrence, type structures, population distribution among different age groups, level education and physical distance hospitals (or medical care centers) categorizing them four indicators geotechnical, structural, social needed facilities from dangerous ones...
Since it is not possible to determine the exact time of a natural disaster’s occurrence and amount physical financial damage on humans or environment resulting from their event, decision-makers need identify areas with potential vulnerability in order reduce future losses. In this paper, GIS-based open source software entitled Seismic-Related Vulnerability Calculation Software (SEVUCAS), based Step-wise Weight Assessment Ratio Analysis (SWARA) method geographic information system, has been...