Abstract

Climate change has introduced significant uncertainties in hydrological systems, such as erratic rainfall, prolonged droughts, and extreme weather events, which have challenged the efficacy of traditional water infrastructure and forecasting models. Conventional hydrological models, whether statistical or physical, often struggle to handle real-time variables, intricate relationships, and nonlinear dynamics. In this context, artificial intelligence (AI) emerges as a powerful, data-driven solution that consistently outperforms traditional methods in both accuracy and adaptability. This study systematically reviews the use of AI, specifically machine learning techniques like Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Random Forests, and Support Vector Machines (SVM), to predict critical hydrological variables such as river flow and groundwater levels. These models are highly capable of learning from noisy or incomplete data, making them particularly valuable for regions with limited monitoring infrastructure. A case study on river flow prediction demonstrates the superior performance of the LSTM model over the statistical ARIMA model, especially in accurately capturing peak flows during extreme events, with an NSE value of 0.87 compared to ARIMA's 0.68. The research also highlights the importance of climate-adaptive infrastructure planning. By integrating AI models with remote sensing data and IoT-enabled environmental monitoring systems, it is possible to create adaptive systems that can anticipate climate change impacts, optimize water storage and distribution, and respond effectively to real-time changes. This approach provides a robust framework for designing water infrastructure that is both flexible and resilient against long-term climatic shifts and short-term extreme events. Despite the transformative potential of AI, significant challenges remain, including data scarcity, algorithmic limitations such as high computational demands and the "black box" nature of some models, as well as ethical concerns regarding potential biases in resource distribution. This work addresses these challenges while emphasizing the promising opportunities presented by AI, including the optimization of water consumption and the development of risk-informed strategies. Ultimately, this paper advocates for an integrated and intelligent approach that redefines hydrological modeling and infrastructure design in the age of climate change.