Perspective Chapter: Big Data and Deep Learning in Hydrological Modeling

Abstract

This chapter delves into the integration of physical mechanisms with deep learning models to enhance the interpretability and accuracy of hydrological process modeling. In the era of big data and rapid advancements in AI, the synergy between traditional hydrological principles and machine learning opens new opportunities for improved water resource management, flood prediction, and drought monitoring. The chapter presents a comprehensive framework that leverages vast datasets from sources such as remote sensing, reanalysis data, and in situ monitoring. It explores the potential of deep learning models, particularly when combined with physical insights, to address challenges in data-scarce regions, improving the accuracy and transparency of predictions. By analyzing the strengths and limitations of current approaches, the study highlights the value of hybrid models in balancing accuracy and interpretability. These models not only enhance predictive performance but also provide more transparent insights into the underlying hydrological processes. This integration contributes to sustainable water management, disaster resilience, and climate adaptation, pushing forward both scientific progress and practical applications. The chapter offers valuable methodologies and case studies that underscore the importance of domain knowledge in the development of explainable and reliable predictive models, reshaping the future of hydrological forecasting.