How Data Science Helps Conquer the Global Water Crisis

Data science and data analytics are fields of scientific research focused on acquiring information and generating meaningful insights from it. In recent years, technologies and methodologies in computer science have grown rapidly, giving researchers an unprecedented view of the scope and causes of major global issues. Today, data scientists are able to use the work they’ve collected to discover ways to address global crises.

One of the most critical problems that needs to be addressed is the global water crisis. Many people with running water in their homes underestimate how their lives would be impacted without easy access to such an essential resource. Nevertheless, much of the human population is without this simple convenience. If you’ve been affected by water scarcity, you understand how getting clean drinking water can become a major part of your life, necessitating long trips each day — if the resource is available at all.

How is data science improving this worldwide situation? This article will help you understand the roles data science plays in identifying the causes of water scarcity, monitoring real-time access to water, and forecasting water quality in specific regions. Further, it will delve into how this information may affect the future of the water crisis. Read on if you want to learn more about how this science may lead to cleaner, more easily accessible water around the world.

Data science has revealed some alarming facts about the current water crisis. Statistics indicate that the shortage of clean and sustainable water is an ever-growing problem in the developing world:

These figures paint a dire picture of the current situation. Fortunately, the skills of data experts and the new tools they are developing can improve the overall situation in a number of different ways. 

Getting real-time data on water quality and availability is vital to taking action before it is too late. There are several methods that can be employed to do this.

Water Quality Monitoring

One strategy involves using sensors in the water supply, or taking water samples, to detect changes in water quality. There are different parameters of drinking water to consider, including pH levels, temperature, dissolved oxygen, salinity, and the presence of contaminants like nitrates. When changes occur, data scientists can track them. If trends suggest that water may soon (or already has) become non-potable, humanitarian organizations and governments can take action. This may involve water purification methods or finding alternative sources of water.

Well Water Flow Tracking

Another approach to gathering information involves measuring the amount of water of wells and the strength of stream flows in areas at risk of water shortages. This often involves the use of water sensors as well. If data indicates that the area may experience a water shortage, preventive measures can be taken to avoid potentially serious consequences.

Local Water Usage Trend-Mapping

A third method is to gather sociological data and surveys about water use in areas at risk of shortages. In conjunction with the methods above, this information can help researchers understand if there is enough clean water in an area to meet the hydration and sanitation needs of the population.

These are but a few of the real-time resource monitoring methods used by data scientists. As you can see, these approaches can give a fuller picture of water availability and use in at-risk areas than ever before.

Data science and analysis can be used to identify issues that may impact water supplies, as well as whether such concerns will affect other regions. Using information from the monitoring methods discussed above, in addition to data on epidemics, researchers may be able to determine the source of diseases. This can help them stop epidemics from spreading further.

For instance, consider the cholera outbreak in Haiti. After impacting the population for nine years, resulting in the death of nearly 10,000 individuals, this water-borne disease was stopped through a concerted effort between the Pan American Health Organization and the Haitian government. This involved the use of thorough surveillance and data analysis to help individuals at treatment centers, make wise investments in clean water, and educate the public on prevention methods.

This is but one example. There are many issues affecting populations around the globe, and they don’t only affect populations in developing areas. The water crisis in Flint, Michigan, demonstrates that even the U.S. is vulnerable. If you want to see warnings for outbreaks near you, you can view an interactive, up-to-date map of current water-borne outbreaks, among others, at HealthMap. This is a tool created by the Boston Children’s Hospital that pulls outbreak alerts from nearly a dozen international sources.

As the human population continues to grow, so does the amount of water that is being used, whether it is needed for everyday life for drinking or sanitation purposes, or for industrial purposes like agriculture and manufacturing. When it comes to the former, data can be used to create more efficient technologies, as well as to educate consumers on smarter water use. 

Data can also help industries conserve water. It can be used to analyze the amount that is currently being used by utility providers, then to inform strategies for reducing its use. The Census Bureau estimates that reducing industrial water intake would save around 222 million gallons of water, which is enough water for more than 2 million people. This can benefit companies’ bottom lines while preventing water shortages in areas where water scarcity may be present.

As practices and methodologies in data science continue to evolve, so will our approaches to detecting and resolving global water crises, including shortages or water-borne disease outbreaks. When it comes to information about water availability and use, more data is being generated every day. This will continue into the future, giving experts an even fuller picture of the water needs of populations at an international level.

Further, artificial intelligence will play an increasingly prominent role in water crisis management. In order to process large sets of information, data science and analytics has seen an increasing reliance on leveraging AI. This will result in new, automated methods of monitoring water quality and water flow. Further, AI can help us optimize current water resources and create comprehensive distribution networks, reducing the number of individuals without access to water. 

Two notable examples of data science projects designed to address the water crisis include:

Such projects will require new generations of skilled data scientists with advanced degrees from accredited schools, as well as individuals with cybersecurity expertise. The legislative efforts needed to implement such projects at scale will necessitate people with data analytics knowledge in roles where they can impact public policy for the better. By using this information to understand the problems associated with the water crisis, as well as to formulate potential solutions, we will inch closer to a future without these concerns.