Application of Machine Learning in Hydrological Monitoring

Dear Colleagues,

The integration of machine learning, particularly deep learning, in hydrological monitoring has significantly transformed its efficiency and capability of analyzing and predicting water-related phenomena. This Special Issue, titled "Application of Machine Learning in Hydrological Monitoring", seeks to explore innovative machine learning methodologies specifically tailored for enhancing hydrological data analysis and decision-making processes. Contributions to this Issue will focus on novel machine learning techniques that improve the accuracy of hydrological predictions, optimize data retrieval and management, and enhance disaster analytics and decision support systems. We invite research that utilizes AI-driven models for tasks such as flood forecasting, water quality monitoring, streamflow prediction, and rainfall data analysis. Papers may also discuss advancements in data augmentation techniques, including the integration of multi-source data for comprehensive flood modeling and predictions, as well as conversational AI approaches using large language models (LLMs). By bringing together these focused studies, the Special Issue aims to highlight the transformative impact of machine learning in hydrology and encourage further interdisciplinary collaborations to advance water resource management.

Dr. Yusuf Sermet
Guest Editor

Dr. Zhouyayan Li
Guest Editor Assistant

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