A review of ensemble learning algorithms used in remote sensing applications
Y Zhang, J Liu, W Shen - Applied Sciences, 2022 - mdpi.com
Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …
A review on flood management technologies related to image processing and machine learning
HS Munawar, AWA Hammad, ST Waller - Automation in Construction, 2021 - Elsevier
Flood management, which involves flood prediction, detection, mapping, evacuation, and
relief activities, can be improved via the adoption of state-of-the-art tools and technology …
relief activities, can be improved via the adoption of state-of-the-art tools and technology …
Deep learning-based landslide susceptibility mapping
Landslides are considered as one of the most devastating natural hazards in Iran, causing
extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas …
extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas …
[HTML][HTML] Multi-hazard susceptibility mapping based on Convolutional Neural Networks
Multi-hazard susceptibility prediction is an important component of disasters risk
management plan. An effective multi-hazard risk mitigation strategy includes assessing …
management plan. An effective multi-hazard risk mitigation strategy includes assessing …
Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India
Agricultural droughts are a prime concern for economies worldwide as they negatively
impact the productivity of rain-fed crops, employment, and income per capita. In this study …
impact the productivity of rain-fed crops, employment, and income per capita. In this study …
[HTML][HTML] Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling
Urban flooding is a major problem for cities around the world, with significant socio-
economic consequences. Conventional real-time flood forecasting models rely on …
economic consequences. Conventional real-time flood forecasting models rely on …
Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions
Precise estimation of reference evapotranspiration (ET0) is crucial for efficient agricultural
water management, crop modelling, and irrigation scheduling. In recent years, the data …
water management, crop modelling, and irrigation scheduling. In recent years, the data …
Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration
For developing countries, scarcity of climatic data is the biggest challenge, and model
development with limited meteorological input is of critical importance. In this study, five data …
development with limited meteorological input is of critical importance. In this study, five data …
Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis
Flood resilience assessment is an important step for any community as it gives the actual
scenario of its ability to resist and recover from flood disasters. However, operationalising …
scenario of its ability to resist and recover from flood disasters. However, operationalising …
[HTML][HTML] Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining
This study presents a novel approach for urban flood forecasting in drainage systems using
a dynamic ensemble-based data mining model which has yet to be utilised properly in this …
a dynamic ensemble-based data mining model which has yet to be utilised properly in this …