AI sparks new hope for cities hit by drought and water stress

While the technological promise of AI is clear, the study highlights major constraints to deployment, particularly in the realms of energy consumption, environmental sustainability, and digital equity.

Cities around the world are grappling with escalating water crises due to climate change. However, artificial intelligence could offer scalable solutions - if deployed strategically, governed ethically, and paired with resilient infrastructure, according to a new study published in Water.

Titled "Towards Resilient Cities: Systematic Review of the Literature on the Use of AI to Optimize Water Harvesting and Mitigate Scarcity", the study explores the rapidly expanding role of artificial intelligence in addressing global water scarcity. It aims to evaluate the effectiveness, integration, and governance of AI-based water systems to support sustainable urban development.

From real-time monitoring and leak detection to rainfall prediction and smart infrastructure optimization, AI emerges as a keystone in global adaptation strategies. Yet, deployment hurdles persist: energy demands, ethical risks, and inadequate policy frameworks threaten to undermine progress.

How is AI being applied to optimize water harvesting and distribution?

The review categorises AI’s water-sector applications into three primary domains: water harvesting optimization, hydrological modeling, and real-time monitoring systems. Across these areas, AI models are enabling unprecedented efficiency in both urban and rural contexts.

Machine learning models such as Random Forest, XGBoost, LSTM, and ARIMA are being used to forecast rainfall, model flood risks, predict short-term water demand, and assess drought severity. Deep learning algorithms help governments and utilities anticipate stormwater runoff and manage catchment basins with real-time control (RTC) technologies. By releasing or storing water in anticipation of rain events, these systems maximize storage efficiency and mitigate flooding.

AI also enhances catchment infrastructure design. Optimization algorithms, including fuzzy clustering and genetic algorithms, support site selection and tank sizing to balance capacity with cost. These systems have been successfully deployed in areas ranging from semi-arid farms to urban rooftops.

Another application is AI-based hydrology analysis. Neural networks now model groundwater fluctuations, estimate soil moisture content, and even simulate nitrate leaching. Explainable AI (XAI) further improves interpretability by identifying influential variables across time and geography, bolstering adaptive management in hyper-arid or mountainous regions.

The integration of Internet of Things (IoT) devices and embedded sensors enables real-time water quality monitoring and leak detection. These systems are increasingly being deployed across municipal and industrial networks, aided by cloud infrastructure and low-power communication protocols like NB-IoT.

What are the challenges in implementation and governance?

The study highlights major constraints to deployment, particularly in the realms of energy consumption, environmental sustainability, and digital equity.

The so-called "energy-water nexus" is a critical concern. AI and IoT systems consume significant energy, often powered by fossil-fuel grids. For example, training large language models like GPT-3 can consume more than 5 million liters of water, used mainly in cooling data centers. This exacerbates the very water scarcity these systems aim to alleviate. The study recommends mitigating these impacts by adopting lightweight algorithms, offloading computation to renewable-powered edge devices, and employing perimeter computing to minimize cloud dependency.

Regulatory challenges are equally daunting. The review notes that governance of AI in water management is fragmented and often absent. Although regions like the EU have introduced comprehensive legislation (such as the AI Act 2024/1689), enforcement mechanisms and adaptation to local contexts remain insufficient. The study proposes a rights-based ethical framework to ensure transparency, inclusivity, and contextual relevance, especially in regions vulnerable to climate extremes and lacking strong institutions.

Another major concern is the widening digital divide. Without equitable access to AI infrastructure and technical capacity, rural and underserved communities risk exclusion from resilience planning. This disparity is amplified by global power asymmetries in AI development, where tools designed in the Global North may not align with the socio-environmental realities of the Global South.

What are the policy and design recommendations for building water-resilient cities?

The study emphasizes that technological solutions must be embedded in systemic, interdisciplinary frameworks. It calls for a shift from isolated AI deployments to integrated water governance models that balance environmental protection, social equity, and economic efficiency.

One recommendation is the adoption of hybrid urban water systems (SUAHs), which combine centralized networks with decentralized infrastructure like rainwater harvesting (RWH), gray-water reuse, and smart detention basins. These systems enhance resilience by offering redundancy and modular scalability.

Moreover, the Viable System Model (VSM) is proposed as a governance framework for cities with limited capacity. By modularizing urban water systems into subsystems (e.g., collection, distribution, and governance) and enabling decentralized decision-making, VSM allows for more adaptive and locally appropriate solutions.

The study also underscores the importance of citizen participation. Trust-building in water technology networks is essential to ensure uptake and long-term sustainability. Initiatives that include communities in monitoring, data collection, and decision-making are more likely to succeed.

On the technological front, the review advocates for the continued development of AI-augmented tools that integrate rainfall forecasting, smart metering, and real-time feedback loops. Specific attention is paid to the growing role of cloud seeding technologies, desalination powered by renewables, and adsorption-based water purification, all enhanced by AI for control and predictive modeling.

Furthermore, resilience indicators must be used to assess water systems under stress. These include recovery time from disruptions, operational redundancy, hydrological adaptability, and energy sustainability. AI can support these metrics through predictive analytics, helping cities anticipate and respond to climate-induced shocks.