Optimisation of ecological landscape water resources management based on deep learning

This study proposes a deep learning-based model to optimise water resource management in ecological landscapes. Traditional approaches struggle with dynamic environmental conditions and increasing water demand. By analysing multidimensional data such as climate, soil moisture, and plant needs, the model dynamically adjusts water distribution. Results show a 92.4% prediction accuracy and a 0.023 mean square error, significantly improving water efficiency and reducing waste. The model’s capacity to process large-scale data supports personalised irrigation strategies and intelligent park management. This research offers practical solutions for green city development and provides technical support for sustainable water use. Beyond accurate prediction, the model advances practice by fusing spatial imagery and ground sensors in a single interpretable pipeline and by validating performance across distinct climate zones, thereby addressing reproducibility and transferability gaps in prior work.