A5003837104- Flood Risk Assessment and Management
- Groundwater and Watershed Analysis
- Hydrological Forecasting Using AI
- Land Use and Ecosystem Services
- Hydrology and Watershed Management Studies
- Coastal wetland ecosystem dynamics
- Landslides and related hazards
- Climate change impacts on agriculture
- Hydrology and Drought Analysis
- Economics of Agriculture and Food Markets
- Fire effects on ecosystems
- Soil and Land Suitability Analysis
- Climate variability and models
- Urban Heat Island Mitigation
- Oil Palm Production and Sustainability
- Water Quality Monitoring Technologies
- Climate Change Policy and Economics
- Water Quality and Pollution Assessment
- Tropical and Extratropical Cyclones Research
- Hydrology and Sediment Transport Processes
- Rice Cultivation and Yield Improvement
- Aluminum Alloys Composites Properties
- Climate Change, Adaptation, Migration
- Advanced Battery Technologies Research
- Metallurgical Processes and Thermodynamics
Bangladesh University of Engineering and Technology
2021-2025
University Research Co (United States)
2021-2023
University of Science and Technology
2023
Chandigarh University
2023
Gopalganj Science and Technology University
2023
Bangamata Sheikh Fojilatunnesa Mujib Science and Technology University
2023
Steel Authority of India Limited
2021
National Institute of Technology Rourkela
2021
University of Engineering and Technology Lahore
2021
Bangladesh Institute of Development Studies
2021
Abstract Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring highlight identification relative importance climatic attributes estimation seasonal intensity frequency Bangladesh. With period forty...
In the face of rapid urbanization, understanding and managing changes in Land Use Cover (LULC) is crucial for sustainability resilience urban ecosystems. Rapid growth resulting from LULC, can lead to fragmentation degradation Ecosystem Services (ES), including habitat provision, water regulation, air purification. This study investigates LULC Dhaka City, Bangladesh, 2010 2021 forecasts their future impacts on ES 2030 2040. Landsat images were processed using Google Earth Engine platform, a...
Abstract The present study aimed to create novel hybrid models produce groundwater potentiality (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) a ROC-based weighting approach have been used for creating integrated GWP. GWP was then verified using both parametric nonparametric receiver operating characteristic curves (ROC),...
Understanding and predicting CO2 emissions from individual power plants is crucial for developing effective mitigation strategies. This study analyzes forecasts an engine-based natural gas-fired plant in Dhaka Export Processing Zone (DEPZ), Bangladesh. also presents a rich dataset ELM-based prediction model Utilizing of Electricity generation Gas Consumption, tons are estimated based on the measured energy use, ELM models were trained data January 2015 to December 2022 used forecast until...
Purpose The present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and subspace (RSS). Design/methodology/approach RF RSS models have been implemented integrating 14 selected condition parametres with inventories generating GPMs. GPM were then validated using empirical bionormal receiver operating characteristics (ROC) curve. Findings very high (831–1200 km 2 )...
Urban floods have become a pressing concern as cities worldwide face unprecedented flooding events that severely impact the lives and livelihoods of millions in densely populated areas. This issue is particularly alarming developing countries, where rapid, unplanned urbanization often outpaces development adequate infrastructure. Climate change exacerbates this challenge by intensifying frequency severity extreme rainfall events, further straining fragile urban systems. Given growing...
ABSTRACT Landslides pose significant hazards in the mountainous region of Sikkim, India, necessitating accurate susceptibility mapping to mitigate risks. This study applies four machine learning models: Boosted Tree (BT), Gradient Boosting Machine (GBM), K‐Nearest Neighbour (KNN), and Multilayer Perceptron (MLP) develop a detailed landslide map. Feature selection was performed using correlation analysis, Boruta model, multicollinearity tests, which identified 13 key conditioning factors...
The study aims to create a novel artificial intelligence model-based landslide susceptibility model (LSM) at Aqabat, Saudi Arabia. For LSM, combination of bagging, dagging, random forest (RF) ensemble with locally weighted learning (LWL), viz. bagging-LWL, dagging-LWL, and RF-LWL has been developed. 50 areas were divided into two categories training (40) testing (10). datasets, the LWL-Bagging had highest AUC value ROC curve (AUC-0.91), followed by LWL-RF (AUC-0.881), LWL-Dagging (AUC-0.88)...