Flood prediction using machine learning models: Literature review
Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …
The research on the advancement of flood prediction models contributed to risk reduction …
Applications of hybrid wavelet–artificial intelligence models in hydrology: a review
Accurate and reliable water resources planning and management to ensure sustainable use
of watershed resources cannot be achieved without precise and reliable models …
of watershed resources cannot be achieved without precise and reliable models …
Groundwater level prediction using machine learning algorithms in a drought-prone area
Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life,
and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in …
and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in …
Prediction of estuarine water quality using interpretable machine learning approach
S Wang, H Peng, S Liang - Journal of Hydrology, 2022 - Elsevier
Estuaries are principal sources of pollution in coastal areas. Estuarine water quality
prediction models can provide early warnings to prevent major disasters in coastal …
prediction models can provide early warnings to prevent major disasters in coastal …
Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting
Drought prediction is an important subject, particularly in drought-hydrology, and has a key
role in risk management, drought readiness and alleviation. Hydrological time series data …
role in risk management, drought readiness and alleviation. Hydrological time series data …
Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins
Abstract Launched in May 2018, the Gravity Recovery and Climate Experiment Follow‐On
mission (GRACE‐FO)—the successor of the erstwhile GRACE mission—monitors changes …
mission (GRACE‐FO)—the successor of the erstwhile GRACE mission—monitors changes …
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
Accurate and reliable forecasting models for electricity demand (G) are critical in
engineering applications. They assist renewable and conventional energy engineers …
engineering applications. They assist renewable and conventional energy engineers …
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
Long-term forecasting of any hydrologic phenomena is essential for strategic environmental
planning, hydrologic and other forms of structural design, agriculture, and water resources …
planning, hydrologic and other forms of structural design, agriculture, and water resources …
[HTML][HTML] A review of the use of artificial intelligence methods in infrastructure systems
L McMillan, L Varga - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the
growth of digitalisation and has the potential to enable the 'system of systems' approach …
growth of digitalisation and has the potential to enable the 'system of systems' approach …
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
As the volatility of electricity demand increases owing to climate change and electrification,
the importance of accurate peak load forecasting is increasing. Traditional peak load …
the importance of accurate peak load forecasting is increasing. Traditional peak load …