Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

Abstract

Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These hybrid models, often integrated with optimization algorithms and fuzzy logic frameworks, significantly improve the predictive accuracy and generalization capabilities of ET estimation. The growing adoption of advanced evaluation metrics, such as Kling-Gupta Efficiency (KGE) and Taylor Diagrams, highlights the increasing demand for more robust performance assessments beyond traditional methods. Despite the promising results, challenges remain, particularly regarding model interpretability, computational efficiency, and data scarcity. Future research should prioritize the integration of interpretability techniques, such as attention mechanisms, Local Interpretable Model-Agnostic Explanations (LIME), and feature importance analysis, to enhance model transparency and foster stakeholder trust. Additionally, improving HANN models’ scalability and computational efficiency is crucial, especially for large-scale, real-world applications. Approaches such as transfer learning, parallel processing, and hyperparameter optimization will be essential in overcoming these challenges. This study underscores the transformative potential of HANN models for precise ET estimation, particularly in water-scarce and climate-vulnerable regions. By integrating CNNs for automatic feature extraction and leveraging hybrid architectures, HANNs offer considerable advantages for optimizing water management, particularly agriculture. Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.