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Artificial Intelligence (AI), including models like ChatGPT, is transforming water resources management by improving hydrological modeling, water quality assessment, and flood prediction. AI techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have enhanced streamflow predictions and groundwater management, particularly in data-scarce regions. AI-powered systems like Smart Microclimate Control Systems (SMCS) optimize agricultural practices, leading to better resource conservation and higher crop yields. However, the scalability and applicability of AI across diverse environments pose challenges, especially where data is limited. The success of AI models depends on data quality, requiring ongoing interdisciplinary research to refine these technologies for real-world use. Additionally, tools like ChatGPT, while valuable for knowledge dissemination and data analysis, raise concerns about accuracy in critical decision-making contexts. In conclusion, while AI offers significant potential for improving water resources management, addressing challenges related to data quality, model scalability, and interdisciplinary collaboration is essential for achieving sustainable and effective outcomes.
Haider et al. (Tue,) studied this question.