A5043053868- Flood Risk Assessment and Management
- Hydrology and Watershed Management Studies
- Hydrological Forecasting Using AI
- Irrigation Practices and Water Management
- Landslides and related hazards
- Hydrology and Drought Analysis
- Groundwater and Watershed Analysis
- Tropical and Extratropical Cyclones Research
- Soil and Unsaturated Flow
- Tree Root and Stability Studies
- Plant Physiology and Cultivation Studies
- Cryospheric studies and observations
- Dam Engineering and Safety
- Remote Sensing and LiDAR Applications
- Soil erosion and sediment transport
- Maritime Ports and Logistics
- Photovoltaic System Optimization Techniques
- Solar Thermal and Photovoltaic Systems
- Land Use and Ecosystem Services
- Travel-related health issues
- solar cell performance optimization
- Geotechnical Engineering and Analysis
- Solar Radiation and Photovoltaics
- Evaluation Methods in Various Fields
- Species Distribution and Climate Change
Hanoi University of Mining and Geology
2017-2024
Duy Tan University
2019-2023
Can Tho University
2021-2022
North Carolina Agricultural and Technical State University
2022
Can Tho University of Medicine and Pharmacy
2021
Ton Duc Thang University
2020
Flash floods are widely recognized as one of the most devastating natural hazards in world, therefore prediction flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology spatial based on Sentinel-1 SAR imagery hybrid machine learning technique. The used to detect flood inundation areas, whereas technique, which firefly algorithm (FA), Levenberg⁻Marquardt (LM) backpropagation, an artificial neural network (named FA-LM-ANN), was...
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),...
The aim of this research is to introduce a novel ensemble approach using Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR), and random forest (RF) models for groundwater-potential mapping (GWPM) in Bastam watershed, Iran. This region suffers from freshwater shortages the identification new groundwater sites critical need. Remote sensing geographic information system (GIS) were used reduce time financial costs rapid assessment resources. Seventeen...
This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In regard, geospatial database the flood with 178 locations 10 predictors prepared used AHP FR were processing coding into numeric format, whereas DNN, which is powerful state-of-the-art probabilistic machine leaning, employed build...
Although snow avalanches are among the most destructive natural disasters, and result in losses of life economic damages mountainous regions, far too little attention has been paid to prediction avalanche hazard using advanced machine learning (ML) models. In this study, applicability efficiency four ML models: support vector (SVM), random forest (RF), naïve Bayes (NB) generalized additive model (GAM), for mapping, were evaluated. Fourteen geomorphometric, topographic hydrologic factors...
Flash flood is one of the most dangerous natural phenomena because its high magnitudes and sudden occurrence, resulting in huge damages for people properties. Our work aims to propose a state-of-the-art model susceptibility mapping flash using decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely HFPS-RSTree model. In this work, we used data from inventory map consisting 1866 polygons derived Sentinel-1 C-band synthetic aperture radar...
The uncertainty of flash flood makes them highly difficult to predict through conventional models. physical hydrologic models prediction any large area is very compute as it requires lot data and time. Therefore remote sensing based (from statistical machine learning) have become popular due open access lesser times. There a continuous effort improve the accuracy these introducing new methods. This study focused on modeling novel hybrid learning models, which can accuracy. ensemble...
Gully erosion has become one of the major environmental issues, due to severity its impact in many parts world. directly and indirectly affects agriculture infrastructural development. The Golestan Dam basin, where soil degradation are very severe problems, was selected as study area. This research maps gully susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), general linear model (GLM). Of 1042 locations,...
Corona viruses are a large family of that not only restricted to causing illness in humans but also affect animals such as camels, cattle, cats, and bats, thus affecting group living species. The outbreak virus late December 2019 (also known COVID-19) raised major concerns when the started getting tremendous. While first case was discovered Wuhan, China, it did take long for disease travel across globe infect every continent (except Antarctica), killing thousands people. Since has become...
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...
Abstract Flash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This study investigates efficacy Deep 1D-Convolutional Neural Networks (Deep 1D-CNN) in spatially predicting flash floods, with a specific focus on frequent tropical cyclone-induced Thanh Hoa province, North Central Vietnam. The 1D-CNN was structured four...