A review of deep learning and machine learning techniques for hydrological inflow forecasting
SD Latif, AN Ahmed - Environment, Development and Sustainability, 2023 - Springer
Conventional machine learning models have been widely used for reservoir inflow and
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …
[HTML][HTML] Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia
Abstract Study region Lower Baro River, Ethiopia. Study focus This paper presents the
novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo …
novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo …
Review of wavelet denoising algorithms
Although there has been a lot of progress in the general area of signal denoising, noise
removal remains a very challenging problem in real-world communication systems …
removal remains a very challenging problem in real-world communication systems …
Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks
Two-dimensional hydrodynamic models are computationally expensive. This drawback can
limit their application to solving problems requiring real-time predictions or several …
limit their application to solving problems requiring real-time predictions or several …
Medium-long-term prediction of water level based on an improved spatio-temporal attention mechanism for long short-term memory networks
Y Wang, Y Huang, M Xiao, S Zhou, B Xiong, Z Jin - Journal of Hydrology, 2023 - Elsevier
River water level usually given by nonlinear and nonstationary time series and affected by
numerous complex spatial and temporal factors. But not all input factors are positively …
numerous complex spatial and temporal factors. But not all input factors are positively …
[HTML][HTML] Ensemble neural networks for the development of storm surge flood modeling: A comprehensive review
This review paper focuses on the use of ensemble neural networks (ENN) in the
development of storm surge flood models. Storm surges are a major concern in coastal …
development of storm surge flood models. Storm surges are a major concern in coastal …
[HTML][HTML] Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment
This paper presents a novel framework for smart integrated risk management in arid regions.
The framework combines flash flood modelling, statistical methods, artificial intelligence (AI) …
The framework combines flash flood modelling, statistical methods, artificial intelligence (AI) …
[HTML][HTML] A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon
Flood crises are the consequence of climate change and global warming, which lead to an
increase in the frequency and intensity of heavy rainfall. Floods are, and remain, natural …
increase in the frequency and intensity of heavy rainfall. Floods are, and remain, natural …
Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches
S Duwal, D Liu, PM Pradhan - Geomatics, Natural Hazards and …, 2023 - Taylor & Francis
Abstract The Karnali River Basin (KRB) comprises the longest river in Nepal, located south
of the Himalayas. Despite its high susceptibility to floods, the basin lacks detailed studies …
of the Himalayas. Despite its high susceptibility to floods, the basin lacks detailed studies …
Potential use of artificial intelligence (AI) in disaster risk and emergency health management: a critical appraisal on environmental health
The risk evaluation of natural disasters is an obstacle to ensuring healthcare services during
catastrophic events worldwide. Therefore, timely and appropriate environmental health risk …
catastrophic events worldwide. Therefore, timely and appropriate environmental health risk …