A review of hybrid deep learning applications for streamflow forecasting
Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …
applications have garnered significant interest in the hydrological community. Despite the …
Artificial intelligence and internet of things (AI-IoT) technologies in response to COVID-19 pandemic: A systematic review
The origin of the COVID-19 pandemic has given overture to redirection, as well as
innovation to many digital technologies. Even after the progression of vaccination efforts …
innovation to many digital technologies. Even after the progression of vaccination efforts …
Short-term rainfall forecasting using cumulative precipitation fields from station data: a probabilistic machine learning approach
Rainfall nowcasting supports emergency decision-making in hydrological, agricultural, and
economical sectors. However, short-term prediction is challenging because meteorological …
economical sectors. However, short-term prediction is challenging because meteorological …
IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
As a complex hydrological problem, rainfall-runoff (RR) modeling is of importance in runoff
studies, water supply, irrigation issues, and environmental management. Among the variety …
studies, water supply, irrigation issues, and environmental management. Among the variety …
[HTML][HTML] A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments
Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers
on water resources, essential for water resources management. The present study aims to …
on water resources, essential for water resources management. The present study aims to …
Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management
DMS Zekrifa, M Kulkarni, A Bhagyalakshmi… - … Applications in Water …, 2023 - igi-global.com
The hydrological cycle is an important process that controls how and where water is
distributed on Earth. It includes processes including transpiration, evaporation …
distributed on Earth. It includes processes including transpiration, evaporation …
Combining two-stage decomposition based machine learning methods for annual runoff forecasting
S Chen, M Ren, W Sun - Journal of Hydrology, 2021 - Elsevier
Accurate annual runoff forecasting is of great significance for water resources management
and timely flood control. However, nonlinear and non-stationary runoff series and the …
and timely flood control. However, nonlinear and non-stationary runoff series and the …
[HTML][HTML] Assessment of influencing factors on non-point source pollution critical source areas in an agricultural watershed
S Wang, Y Wang, Y Wang, Z Wang - Ecological Indicators, 2022 - Elsevier
Abstract Critical Source Areas (CSAs) are areas that contribute disproportionate high levels
of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of …
of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of …
A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region
In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become
popular tools in assisting various aspects of reservoir operation. However, the practical uses …
popular tools in assisting various aspects of reservoir operation. However, the practical uses …
Deep learning approach with LSTM for daily streamflow prediction in a semi-arid area: a case study of Oum Er-Rbia river basin, Morocco
Daily hydrological modelling is among the most challenging tasks in water resource
management, particularly in terms of streamflow prediction in semi-arid areas. Various …
management, particularly in terms of streamflow prediction in semi-arid areas. Various …