Abstract

Climate change has introduced significant uncertainties in hydrological systems, such as erratic rainfall, prolonged droughts, and extreme weather events, challenging 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 solution, providing data-driven modeling capabilities that outperform traditional methods in accuracy and adaptability. This study investigates 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 river flow, groundwater levels, and other hydrological variables. These models excel in learning from noisy or incomplete data, making them suitable for regions with limited monitoring infrastructure. A case study using LSTM models for river flow prediction demonstrates superior performance over ARIMA, particularly in capturing peak flows during extreme events. The research also highlights the importance of climate-adaptive infrastructure planning. By integrating AI models with remote sensing data and IoT-enabled environmental monitoring, adaptive systems can anticipate climate change, optimize water storage and distribution, and respond to real-time changes. This approach offers a robust framework for designing water infrastructure that is resilient and flexible in response to long-term climatic shifts. Despite challenges like data scarcity and algorithmic opacity, AI has transformative potential for sustainable water management. Its ability to process vast datasets and simulate climate-water interactions enables decision-makers to shift from reactive to proactive, risk-informed strategies. This work advocates for an integrated, intelligent approach that redefines hydrological modeling and infrastructure design in the age of climate change.