Hybrid deep learning models for water demand forecasting in greenhouses: Exploring the energy Nexus in Urban agriculture

Le résumé fourni par la source

• A novel LSGAN-CBiLSTM hybrid model for water demand forecasting (WDF) in greenhouses, incorporating the Energy Nexus. • First application of this hybrid deep learning model in WDF for urban agriculture. • LSGAN pre-processes data, removing noise and anomalies for cleaner input. • CNN and BiLSTM layers capture spatial and temporal dependencies for accuracy. • Comparative analysis shows LSGAN-CBiLSTM outperforms other state-of-the-art AI models in WDF. Precise water demand forecasting (WDF) is crucial for sustainable irrigation and resource efficiency in urban greenhouse systems. This study introduces a cutting-edge hybrid deep learning approach designed for short-term WDF, while also considering the energy nexus between water, energy, and environmental factors. The model integrates the least squares generative adversarial network (LSGAN) for data pre-processing and noise reduction, convolutional neural networks (CNN) for feature selection, and bidirectional long short-term memory (BiLSTM) for time-series state modeling, and named as LSGAN-CBiLSTM. Using real-world data from the Wageningen Research Centre in Bleiswijk, Netherlands, the model significantly outperformed benchmark approaches, achieving an R-value of 99.57% with minimal forecasting errors. The model demonstrated exceptional stability, minimal bias, and strong handling of environmental variability, improving short-term WDF accuracy, optimizing water management in urban agriculture, enhancing sustainable irrigation, and addressing the energy nexus for efficient resource use.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.