[HTML][HTML] A critical review of RNN and LSTM variants in hydrological time series predictions

M Waqas, UW Humphries - MethodsX, 2024 - Elsevier
The rapid advancement in Artificial Intelligence (AI) and big data has developed significance
in the water sector, particularly in hydrological time-series predictions. Recurrent Neural …

[HTML][HTML] Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF …

A Elbeltagi, S Heddam, OM Katipoğlu… - Journal of Hydrology …, 2024 - Elsevier
Abstract Study region Chengdu, Wuhan, Chongqing, and Kunming regions in China. Study
focus Accurate estimation of crop water use or actual evapotranspiration (AET) remains a …

[HTML][HTML] Design and Implementation of a Deep Learning Model and Stochastic Model for the Forecasting of the Monthly Lake Water Level

WAH Al-Nuaami, LA Dawod, BMG Kibria… - Limnological …, 2024 - mdpi.com
Freshwater is becoming increasingly vulnerable to pollution due to both climate change and
an escalation in water consumption. The management of water resource systems relies …

Constructing a High Temporal Resolution Global Lakes Dataset via Swin-Unet with Applications to Area Prediction

Y Han, B Huang, H Gao - arXiv preprint arXiv:2408.10821, 2024 - arxiv.org
Lakes provide a wide range of valuable ecosystem services, such as water supply,
biodiversity habitats, and carbon sequestration. However, lakes are increasingly threatened …

Physical-Chemical Properties Prediction of Chao Phraya River using Deep Learning Methods

IWA Suranata, IKAA Aryanto… - … on Smart Computing …, 2024 - ieeexplore.ieee.org
This research aims to develop a predictive model for the water quality of the Chao Phraya
River, focusing on the pH, Dissolved Oxygen (DO), and Electrical Conductivity (EC) values …