Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units
S Poornima, M Pushpalatha - Atmosphere, 2019 - mdpi.com
Prediction of rainfall is one of the major concerns in the domain of meteorology. Several
techniques have been formerly proposed to predict rainfall based on statistical analysis …
techniques have been formerly proposed to predict rainfall based on statistical analysis …
A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method
The important foundation for water resource management and utilization is effective monthly
runoff prediction. In this study, a new coupled model for predicting monthly runoff is …
runoff prediction. In this study, a new coupled model for predicting monthly runoff is …
Directed graph deep neural network for multi-step daily streamflow forecasting
Y Liu, G Hou, F Huang, H Qin, B Wang, L Yi - Journal of Hydrology, 2022 - Elsevier
Reliable and accurate multi-step streamflow forecasting is of vital importance for the
utilization of water resources and hydropower energy system. In this paper, a spatial deep …
utilization of water resources and hydropower energy system. In this paper, a spatial deep …
Short-term runoff prediction using deep learning multi-dimensional ensemble method
Recently, deep learning models have been widely used in water conservancy engineering
forecasting problems, due to their excellent ability to deal with the complex interactions …
forecasting problems, due to their excellent ability to deal with the complex interactions …
Short‐term flood forecasting with a neurofuzzy model
This study explores the potential of the neurofuzzy computing paradigm to model the rainfall‐
runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing …
runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing …
[HTML][HTML] Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh
Time-series analyses of temperature data are important for investigating temperature
variation and predicting temperature change. Here, Mann–Kendall (M–K) analyses of …
variation and predicting temperature change. Here, Mann–Kendall (M–K) analyses of …
Streamflow drought time series forecasting
R Modarres - Stochastic Environmental Research and Risk …, 2007 - Springer
Drought is considered to be an extreme climatic event causing significant damage both in
the natural environment and in human lives. Due to the important role of drought forecasting …
the natural environment and in human lives. Due to the important role of drought forecasting …
Ensemble empirical mode decomposition based deep learning models for forecasting river flow time series
R Maiti, BG Menon, A Abraham - Expert Systems with Applications, 2024 - Elsevier
River flow forecasting is important for flood prediction and effective utilization of water
resources. This study proposed a comprehensive methodology that simultaneously enables …
resources. This study proposed a comprehensive methodology that simultaneously enables …
Comparison of ARIMA and NNAR models for forecasting water treatment plant's influent characteristics
A reliable forecasting model for each Water Treatment Plant (WTP) influent characteristics is
useful for controlling the plant's operation. In this paper Auto-Regressive Integrated Moving …
useful for controlling the plant's operation. In this paper Auto-Regressive Integrated Moving …
Entropy theory for streamflow forecasting
Streamflow forecasting is used in river training and management, river restoration, reservoir
operation, power generation, irrigation, and navigation. In hydrology, streamflow forecasting …
operation, power generation, irrigation, and navigation. In hydrology, streamflow forecasting …