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 …
[HTML][HTML] A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has
further generated immense interest in researching aspects for further improvements to …
further generated immense interest in researching aspects for further improvements to …
A transdisciplinary review of deep learning research and its relevance for water resources scientists
C Shen - Water Resources Research, 2018 - Wiley Online Library
Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …
industries, daily lives, and various scientific disciplines in recent years. DL represents …
[HTML][HTML] Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees
Predictive analytics play an important role in the management of decentralised energy
systems. Prediction models of uncontrolled variables (eg, renewable energy sources …
systems. Prediction models of uncontrolled variables (eg, renewable energy sources …
Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting
This article explores the suitability of a long short-term memory recurrent neural network
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …
Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting
Developing accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …
shoreline maintenance and sustainable water resources planning and management. In this …
Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management
W Niu, Z Feng - Sustainable Cities and Society, 2021 - Elsevier
Accurate runoff forecasting plays an important role in guaranteeing the sustainable
utilization and management of water resources. Artificial intelligence methods can provide …
utilization and management of water resources. Artificial intelligence methods can provide …
Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
Monthly streamflow forecasting is required for short-and long-term water resources
management especially in extreme events such as flood and drought. Therefore, there is …
management especially in extreme events such as flood and drought. Therefore, there is …
Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
D Zhang, J Lin, Q Peng, D Wang, T Yang… - Journal of …, 2018 - Elsevier
Reservoirs and dams are vital human-built infrastructures that play essential roles in flood
control, hydroelectric power generation, water supply, navigation, and other functions. The …
control, hydroelectric power generation, water supply, navigation, and other functions. The …
Support vector machine applications in the field of hydrology: a review
PC Deka - Applied soft computing, 2014 - Elsevier
In the recent few decades there has been very significant developments in the theoretical
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …