Dataset for: Recurrent Neural Networks for Short-Term Water Demand Forecasting: A Technical Review and Framework for Implementation (2026)
Abstract
Structured dataset containing the systematic extraction and classification of 42 peer-reviewed studies on short-term water demand forecasting using recurrent neural networks (standard RNN, LSTM, GRU), including model characteristics, model architecture, preprocessing methods, feature selection, feature engineering, signal decomposition, feature extraction, variables, optimization strategies, tusen hyperparameters, performance indicators, and benchmarking information.
Fields of Study & Subjects
Hydrological Forecasting Using AI
Energy Load and Power Forecasting
Stock Market Forecasting Methods
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 February 2026
- Language
- EN
- Document type
- Dataset