How Can Data Analytics Help Solve the World's Water Crisis?
Published: 2022-12-15 14:08:25


Research in the domains of data science and data analytics is centered on gathering data and drawing actionable conclusions from it. Researchers now have an unrivaled understanding of the scale and root causes of important global crises thanks to the tremendous growth of computer science tools and approaches in recent years. Data scientists are now able to apply the work they've done to find solutions for world challenges.

Many people who have access to running water in their houses are unaware of how their lives would be affected if they lost that convenience. However, a large portion of the human population lacks this basic amenity. If you've ever experienced a water shortage, you know how important it can be to have clean drinking water, even if it means frequent long excursions.

Water Crisis and Data Analytics:

The current water situation has several concerning facts that data analytics has uncovered. According to statistics, the developing world is experiencing an ever-growing challenge with the lack of access to clean, sustainable water.

Around 10% of the world's population, or 844 million people, lack access to adequate drinking water.
2.3 billion people lack access to basic sanitation because of water scarcity.
Each day, women and girls spend 200 million hours lugging water because they are frequently expected to do it in places without access to clean water.
Every day, more than 800 kids under the age of five pass away from dehydration brought on by diarrhea and bad sanitation.

These statistics give a bleak picture of the current state of affairs. Fortunately, the abilities of data analytics and the new technologies they are creating can significantly improve the current state of affairs. To learn more about the latest analytics tools, visit the best data analytics courses, right away! 
Resource Monitoring in Real Time:
Obtaining current water supply and quality information is essential for taking action before it is too late. Many methods can be applied to this.

Monitoring Water Quality:
One method to monitor water quality changes involves installing sensors in the water supply or taking water samples. Considerations for drinking water include its pH, temperature, salinity, dissolved oxygen content, and presence of impurities like nitrates. Data scientists can monitor changes as they take place. Humanitarian organizations and governments can take action if patterns indicate that water may soon (or already has) stop being potable. This could entail locating different water sources or using water purifying techniques.
Water Flow Tracking in Wells:
Measuring the depth of wells and the force of stream flows in places in danger of water shortages is another method of getting information. Additionally, water sensors are frequently used in this process. Preventive steps can be implemented if evidence suggests that there might be a water shortage in the area to avert potentially grave repercussions.

Mapping Local Water Use Trends:
Gathering sociological information and survey results regarding water use in places at risk of shortages is a third approach. This data, along with the techniques mentioned above, can assist researchers in determining whether there is enough clean water in a location to satisfy the population's needs for hydration and sanitation.

These are just a few techniques data scientists employ for real-time resource monitoring. As you can see, these methods can now provide a complete picture of the availability and use of water in at-risk locations.

Finding Problems With the Present Water Supply:
The Pan American Health Organization and the Haitian government worked together to stop the water-borne disease after it had killed approximately 10,000 people and affected the community for nine years. In order to assist people at treatment facilities, make smart investments in clean water, and inform the public about prevention techniques, this required the use of rigorous surveillance and analytics. This is but one instance. Numerous problems impact people worldwide and don't just affect those in developing countries.

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Intelligent Water Use:
The amount of water utilized increases along with the human population, whether it is for domestic uses like drinking and sanitation or for commercial uses like agriculture and manufacturing. When it comes to the former, data may be utilized to develop more effective technologies and inform consumers on how to use water more wisely.

Target Crucial Analytical and Control Objectives:
Different source water characteristics present varied water treatment problems regarding total organic carbon (TOC), pH, turbidity, etc. Solutions that offer real-time in-situ sampling and share data automatically with plant control systems give measurable benefits, regardless of the water chemistry being analyzed or the sensing technology being used:

Quickness Of Response:

Choosing instrumentation that provides the quickest access to data that specifies process flow conditions and enables the quickest decision-making for optimal efficiency is the first step to cost efficiency.

Continuous Compliance with the Law:

Wherever water chemistry modifications are required to meet compliance requirements, avoid penalties for noncompliance, or deliver the best-looking, best-tasting water quality possible, knowing the composition of source water and treated water is crucial.

Utilizing Energy and Chemicals Effectively:

Even with the best instrumentation delivering the most precise, current readings, failing to recognize and respond to actual conditions until after they have occurred can result in missed opportunities for efficiency. The most effective decisions for the overall best control and operating efficiency can be guided by enterprise software for distributed control and asset management, which can respond to even the smallest changes in water chemistries and anticipate future trends in equipment life.

Analyze Data and Draw a logical Conclusion:
While analyzing the long-term performance of both water quality and plant efficiency is necessary for managing long-term operations most efficiently, knowing process characteristics on the spot is essential for delivering quality results. Users can respond to important queries concerning process throughput to improve asset optimization using a modular data analytics created for operations.

Are these pumps currently working at their highest capacity?
Can we get a little bit more capacity out of current resources?
Are there developing trends or patterns that could signal the need to quickly ramp up capacity and empty tanks, such as more capacity flowing via far-flung lift stations as a storm front passes through?

However, process control is just one benefit of automated operation and higher-level analysis. They can also include structural and financial evaluations that affect the long-term viability of the WTP and WWTP infrastructure. Plant managers are able to respond to a greater range of inquiries concerning maintenance and repair for long-term infrastructure asset management as historical process performance data is obtained and examined:

Are there more warnings coming from one process element than before?
Are certain parts of machinery producing too much vibration?
Is this a problem with periodic maintenance or a malfunction of the machinery?
Is the machinery repairable, or does it require replacement?

The asset optimization software is designed to take advantage of the expertise of seasoned water plant operators, decipher sensor readings, foresee upset conditions, and react with automated control like a seasoned plant manager would operate manual controls. Analytical software closes the loop between operations and asset management, enabling plant operators to transition from reactive maintenance to more predictive maintenance, just as PID controls or DCS systems close the control loop on plant processes. 

Based on normalized data and pattern identification from the previous performance, past reactions, and results, asset optimization software can actually assist plant operators in arriving at the most efficient solutions earlier by utilizing machine learning features. If you want to learn more about data science and analytics, have a look at the top data science course with placement, co-developed by IBM and industry leaders.