Climate change is worsening water scarcity across the globe, leading to droughts, wildfires, and crop failures. Also, with growing urbanization and climate change issues, the demand for sustainable water management has increased more than ever. For instance, a recent report by the Global Commission on the Economics of Water states that by the end of the decade, demand for fresh water will outstrip supply by 40%. However, the integration of AI and Big Data has proven to be useful in solving critical issues related to water environment and thereby having a positive impact on the environment and the ecosystem and also on the management of critical resources like water.

Understanding the terms AI and Big Data

• Artificial intelligence (AI) stands for the simulation of human intelligence processes by machines, like computer systems.

• Big data is used to describe large volumes of data – both structured and unstructured.

Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in several environment and water management (EWM) fields, such as smart water and energy management systems, and remote sensing. The convergence of Artificial Intelligence (AI) and Big Data is useful in solving critical issues in the water sector such as detecting leaks and reducing river pollution, crop management, etc.

Major Water Issues Facing the World:

Ø  Scarcity of Clean Drinking Water- Billions of people across the world lack adequate access to one of the essential elements of life, that is clean water. The global warming and climate change continue to pose challenges to the availability of water for consumption and other uses. Despite the efforts by the governments and several groups the problem is projected to aggravate in the coming future and if not monitored may be become a critical issue for the future generation.

The United Nations has predicted that without interventions in climate change, the water scarcity in semi-arid and arid regions will dislocate hundreds of millions of people by 2030.

Even high-income countries like the United States experience water scarcity or water stress.

In 2022, the US administration announced an action plan related to global water security, which focused it on being a critical component of its efforts to achieve U.S. foreign policy objectives. However, the pace of climate change and the COVID-19 pandemic have presented new challenges. At present, several countries have stated that they are unlikely to implement integrated water management systems by 2030, the target date for fulfilling the UN Sustainable Development Goals (SDGs).

Ø  Impact on Agriculture: The agricultural sector depends on water for crop production. However, the unpredictability in rain and soaring temperatures increases the evaporation of water from soil, hence impacting the farming. This also increases the frequency of floods, thereby damaging the crops. Also, water stress in certain regions can make it difficult for farmers to grow crops. Technologies like AI can help farmers in optimizing their irrigation systems based on the data collected on factors such as precipitation, humidity, etc.

Efficient management and conservation of water resources is essential, and experts claim that circularity of water systems is as important as the road to Net Zero.

Ø  Wildfire and Drought: Wildfires and drought in 2010 wiped out Russian crops, which resulted in an increase in commodities prices and food riots in Egypt and Tunisia at the start of the Arab uprisings. Within the European Union (EU), both the frequency and severity of droughts have risen, impacting nearly 20% more areas and people between 1976 and 2006. Therefore, efficient water management is important and helps in securing a consistent water supply for human consumption, industry and agriculture, and also enhancing the ability to withstand water shortages caused by climate fluctuations.

According to TechSci Research report, “Big Data Analytics Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028, Segmented By Component (Solutions and Services), By Deployment Mode (On-Premises, Cloud and Hybrid), By Application (Risk & Fraud Analytics, Enterprise Data Warehouse Optimization, Internet of Things, Customer Analytics, Operational Analytics, Security Intelligence and Others), By Organization Size (Large Enterprises and SMEs), By Industry (BFSI, Healthcare, Government, IT & Telecom, Manufacturing, Retail and Others), By Region, Competition,” the Global Big Data Analytics Market is expected to reach USD 304. 57 billion by the end of 2023, with a compound annual growth rate (CAGR) of 14.81% during the forecast period. The growth of the market can be attributed to the increasing adoption of data driven decision making as enterprises across various industries focus on extracting insights and optimize business operations. Moreover, the digital footprint provides a rich source of information that can be used for target marketing and offering better customer experience. Also, the growing adoption of cloud computing is contributing to the market growth.

The Role of AI and Big Data in Water Management

The use of AI and Big Data technologies have made far-reaching changes in the monitoring and prediction of water-related phenomena. One of the best examples of this is the use of satellite imagery, sensors, and machine learning for tracking water quality, detecting pollution levels, and predicting harmful algal blooms. These real-time insights enable policy makers to take preliminary measures, and hence protect both the public health and ecosystems. For instance, in South-West England, a pilot project in Devon is using AI to predict and prevent water pollution. The data on local environmental conditions can be gathered by connected sensors in rivers and fields, and then combined with satellite imagery. This helps in the real time identification of pollution from sources such as agricultural runoff and sewage discharges, and hence timely action on preventative measures.

Machine Learning (ML) Models for Water and Wastewater Treatment

In addition to optimizing water treatment operations, in a recent research Machine Learning (ML) was applied to estimate the count of microplastic particles based on aggregate particle weight measurements. The research focused on saving the large amount of time required for manual counting of microplastics.

Real Time Monitoring of Water Quality

The traditional water quality monitoring systems are outdated and expensive, and the accuracy of the readings is questionable. However, AI and big data, can help in analyzing large amounts of data accurately and quickly, detect patterns and anomalies, and provide real-time information about water quality. This helps water management organizations to make more informed decisions about water treatment processes, and thereby decrease the risk of contaminations and ensuring public safety.

Reducing Water Wastage

AI and big data can also help to reduce water wastage. In many cities worldwide, huge amounts of fresh water are lost due to leakages in infrastructure systems. AI and big data can monitor these systems continually and detect potential leakages before they become significant problems. The data collected from these systems can provide useful insights that help water management organizations to make decisions about infrastructure maintenance and improve water conservation.

Flood Control and Management

Droughts and Floods are a recurring threat in many parts of the world. These have devastating effects. The flood prediction models empowered by AI can be useful in preventing such occurrences and analyzing historical weather data, river levels, and other relevant information to generate accurate forecasts. These predictions are valuable for disaster preparedness and response, helping to mitigate the impact of floods on communities and infrastructure and thereby saving lives.

Water Conservation

Wastewater Treatment

Treating wastewater is essential to safeguard public health and the environment. AI and Big Data support the development of smarter wastewater treatment plants. Predictive maintenance algorithms help prevent breakdowns, while machine learning models optimize the treatment process, reducing energy consumption and improving the quality of treated water.

Desalination

With the depletion of freshwater sources, desalination plays a pivotal role in providing a sustainable water supply. AI is used to enhance desalination processes by optimizing energy consumption, membrane performance, and plant operations. This results in more cost-effective and environmentally friendly desalination process.

Ecosystem Health

AI-driven models can be used to understand and protect fragile ecosystems in an effective manner. Aquatic ecosystems depend on clean water and stable conditions. AI is used to monitor and protect these environments by tracking changes in water quality, habitat conditions, and species distribution. Moreover, researchers can use these AI-driven models to understand and protect the ecosystems.

Regulations and Decision-Making

Data-driven decision-making is essential in water resource management. The integration of Big Data analytics and AI provides policymakers with insights to generate evidence-based policies and regulations. These tools allow governments and policy makers to make informed decisions about water allocation, quality standards, and infrastructure investments.

Future Trends in AI and Big Data in Water Management

The future is expected to witness more technological advancements and use of AI and Big data in water sector. It is quite evident that in the near future there will be more focus on use of sophisticated AI algorithms, IoT and technologies like blockchain for real time monitoring of water distribution and water quality. All these will also help in management of water pollution levels and enhancing smart irrigation systems.

Challenges and Considerations

While AI and Big Data offer immense promise in water environments, there are several challenges and considerations that need to be taken care of such as data quality, privacy and security, adoption costs and expertise.

Ø  Data Quality: Accurate and reliable data is essential for AI models to perform effectively. Inconsistent or incomplete data can lead to unreliable predictions and decisions.

Ø  Privacy and Security: Protecting sensitive water data from cyber threats is critical. Ensuring data privacy and security is paramount, especially when dealing with water infrastructure.

Ø  Adoption Costs: Implementing AI and Big Data solutions can be expensive. Smaller municipalities or regions may face budget constraints that limit their ability to leverage these technologies.

Ø  Expertise: Utilizing AI and Big Data requires specialized skills and knowledge. Training and retaining experts in this field can be a challenge for organizations.

According to TechSci Research report, “Global Artificial Intelligence Market By Component (Hardware, Software, Services), By Application (Image Recognition, Natural Language Processing, Speech Recognition, Computer Vision, Other), By Business Function (Finance, Security, Human Resource, Law, Marketing & Sales, Operations, Supply Chain Management), By End User (BFSI, Fashion and Retail, Healthcare and Life Sciences, Manufacturing, Automotive, Aerospace and Defense, Construction, Other), By Region, Competition, 2018-2028,” the Global Artificial Intelligence Market was valued at USD135.83 billion by the end of 2022, with a compound annual growth rate (CAGR) of 36.62% during the forecast period. The market is undergoing transformation and AI technologies have the potential to automate tasks and make predictive decisions. For example, in the healthcare sector, the AI is being used to assist in diagnosis and management of diseases.

Conclusion