Global Scenario of Water Crisis

The worldwide demand for water has been increasing at a rate of around 1% per annum over the past decades due to population explosion, economic growth and changing consumption habits, among other factors, and it will continue to grow pointedly over the conceivable future. Industrial and domestic demand for water is increasing much faster than agricultural demand. The massive bulk of growth in demand for water is seen in developing or emerging economies. Beyond all the water on Earth, salt-water in oceans, seas and salty groundwater compose about 97% of it. Only about 2.75% is fresh water, including around 2% frozen in glaciers, ice plus snow, 0.75% as fresh groundwater and soil moisture, and below 0.01% of it as surface water in ponds, swamps, and rivers.

Worldwide Water Reserves

Freshwater lakes cover about 87% of this fresh surface water, counting 29% in the African Great Lakes, 22% reserved in Lake Baikal in Russia, 21% cupped in North American Great Lakes, and 14% in remaining different lakes. Swamplands have most of the balance with only a little in rivers, most particularly the Amazon River. The atmosphere encompasses 0.04% water. In areas with no fresh water on the surface, fresh water extracted from precipitation may, due to its lower density, partly cover saline groundwater in layers. Most of the global freshwater is frozen in ice sheets. Many areas suffer the lack of circulation of fresh water, such as deserts.

Available Freshwater Quantities

The countries with the worst water crisis scenario, which means, countries lying under water scarcity index include Afghanistan, Ethiopia, Chad, Cambodia, Laos, Haiti, Pakistan, Egypt, Syria, and Somalia. It has also been found that Chile, Estonia, Namibia, and Botswana could face a specifically significant increase in water stress in a couple of decades. This means that industries, farms, and people in these countries specifically may be more susceptible to scarcity. Statistics testify that around 14 of the 33 probably will be most water-stressed countries in the coming years in the Middle East. Of which, 9 are considered very highly stressed like Bahrain, Palestine, Qatar, Kuwait, United Arab Emirates, Israel, Oman Saudi Arabia, and Lebanon. The region, already debatably the least water-secure in the world, lures heavily upon groundwater and purified seawater and faces unique water-related challenges in the future. Some countries have already established that decoupling water use from financial sustainability is possible. For instance, in Australia, water consumption decayed by 40% from 2001 to 2009 while the economy grew by an astounding 30%. The International Resource Panel of the UN quotes that governments have tended to bulk investments in generally incompetent solutions including mega-projects like dams, aqueducts, pipelines, canals, and water reservoirs, which are neither environmentally bearable nor economically feasible. The most economical way of decoupling water use from economic growth is for governments to create all-inclusive water management plans that consider the entire water cycle, from foundation to dissemination, sensible use, treatment, reutilizing, and return to the environment.

Refer the following image depicting future water-scarce regions.



Governing Factors of Water Management and How Data Tools can be of Assistance?

The global water demand is increasing so fast that water scarcity is unsettling energy production, causing food shortages, toppling economic development, and frightening political stability. In fact, as indicated in the World Economic Forum Global Risks Report, water crisis comes at the number one worldwide risk and is evolving as a serious threat to citizens, business, environment, and political constancy across the world. The top ten major consumers of abstracted water starting from India, United States of America, China, Pakistan, Iran, Mexico, Bangladesh, Saudi Arabia, Indonesia, and Italy construct around 72% of all water reservoirs worldwide. Groundwater has become fundamental for the means of support and food security of approximately 1.2 to 1.5 billion rural citizens in the inferior regions of Africa and Asia. Among 75% of a billion people have no available clean drinking water, and the United Nations guesses that around 3.3 billion people are living in areas of the water crisis; that covers half the population of earth. 80 % of all sickness and ailment is related to tainted water, and by the end of the decade, it is likely that two out of three people will be dwelling in a water-stressed area.

Attitude towards Water Usage

Enterprise data tools can help make better use of present water resources. Over the past century, the global population increased tremendously, while humans’ consumption of water increased by as much as double. Cooking, bathing, cleaning, drinking, and watering plants are some of the principal water uses. The world’s aggregated groundwater perception is estimated at approximately 1,000 cubic km per annum, with 67% for irrigation, 22% for household purposes and 11% for industrial purposes. On the finance side, industries use twice the amount of water than individuals. Utilities, which use huge amounts of water to chill their plants, must be more enthusiastic to invest in investigative tools grounded in big data so as to improve competence and diminish wasteful water use. Most industrial sites already gathered huge amounts of data, but do not know how to examine the data that they have gathered; this is where big data has the perspective to make the greatest impact. Data science can help utility providers get the statistics of their water usage, and work out ways to reduce water wastage with the purpose of reducing their negative impact on the environment, and mainly, the water crisis.

Real-time Monitoring of Resources

Water quality can be pursued in real time using data tools. This diminishes the exertion, interval, and assets required to control whether a given water source is of good eminence. Real-time monitoring can also be used to monitor if accessible water is actually clean and harmless to drink, which can save time, money, and less perceptible resources, such as human work. An example of that is water level governing tools or data registry of a water source that can produce the data algorithm and predict a water shortage at a particular point in time.

Water Quality Forecasting

Data tools and principles can be used to analyze water quality leanings and make predictions regarding planned water quality as affected by rain, contamination, and other contributing factors. Water Quality Forecasting is one way which can determine the available amounts of fresh water. Efficient data analytics tools can help boost water forecasting and derive data of water quality 45% faster.

Water Supply Issues

Data tools can also be applied to recognize whether there are regional or communal issues with the water supply. This can be highly important in terms of avoiding diseases and epidemics that could be spread through water system. Effective monitoring of the water supply system by Data tools is a tangible way of diminishing water issues. Water-oriented sensors can detect a leakage spot or can predict a haul in water supply from master storage, thus reducing the events of clueless incompetency by 85%.

Measuring only what’s Useful

Some treatment plants show the deficiency of important and basic measurements such as DO in the ventilation basins, airflow to each ventilation zone, and electricity use by blowers, but care has to be taken in the eagerness not to smack to the other risk. One can spend pools of money gauging ammonia and nitrate all over a treatment plant, but unless he is actually utilizing it for control, the quantities will eventually be overlooked and the instruments neglected. It is best to have a few good instruments, located in places where they actually measure something one can control, and try to have those water sensors running well. Your water level indicators or sensor equipment is of no use if placed at a wrong location, despite its 98% accuracy.

AI for Water Management

Hydrological concerns like droughts, rainfall, pollutant conveyance, and groundwater management require the accessibility of detailed and precise data about the water systems, which are usually not offered in the developing countries because of economic and infrastructure boundaries. In such thought-provoking scenarios, artificial intelligence can be a friendly substitute for water management. As an example, the UAE uses advanced Artificial Intelligence and enterprise data tools to manage water quantities for citizens. They claim to have managed 22 million gallons of water effectively with AI-based water management systems.

Excessive Loss of Water Resources in Agriculture

Agriculture is unmistakably the largest user and probably waster of water in the world. Farmers consume over 70% of the worldwide freshwater supply, but 60% of it is wasted as a result of seepages in irrigation units and unjustifiable uses.

Population Growth and Water Usage

As the world’s population continues to grow and urbanize, and as the climate changes, experts predict that by two decades from now in excess of 4.8 billion persons, half the global crop production will be in danger due to water stress. The number of hopeful case studies specifies that big data and enterprise data tools could possibly contribute greatly towards better water management in major cities around the world. One such matter is that only 59% out of the world’s total population have the luxury to access and utilize clean water. In some areas, the problem is intimidating. In sub-Saharan Africa, only 16% of the inhabitants have access to potable water. Across the world, some 6.6 billion people have no unswerving access at all to clean water year-round.

Successful Cases of Data Tools for Water Management

However, in spite of the numerous limitations regarding the conservation and scarcity of water, there are some successful efforts which testify in the favor of data tools’ role as a medium of water conservation. Following are some of the success stories of countries’ effort to use enterprise data tools for handling water crisis.

East Africa

Rwanda in Eastern part of Africa did the unexpected. In Rwanda, organizations are exasperating to connect a million farmers to data in somehow. They thought that they need to exercise what info they needed for this to trigger the process of water conservation successfully. By now, open data networks are present where farmers obtain messages if there is a caution they need to be informed about, for example, a climate change.

India

India has logged interesting improvements in the area of water crisis management with the help of AI. Two Artificial Neural Networks (ANN) models were established in India to find water quality of the Gomati River. Water quality variables like pH, COD, TS were considered as vectors and the prediction of Dissolved Oxygen (DO) and Biological Oxygen Demand (BOD) were taken out. The neural network prototype was industrialized using data which had findings for three years. The input vectors were developed by the help of correlation coefficient with DO. Performance of ANN prototypes was equated using correlation coefficient, coefficient of efficiency, and mean squared error. The projected values of DO and BOD were precise.

United States

The state of Illinois, US, has used the closed loop feed-forward training algorithm for the prediction of insecticide citation in groundwater. They used aquifer depth, aquifer sensitivity to pesticide, pesticide leaching and samples for exact time as vectors for these Artificial Neural Networks.

UK

The automatic nursing of flows and pressures in the clean water distribution system allow the system to detect anomalies such as an unexpected drop in pressure. An upsurge inflow at a meter, or multiple meters, may direct a leak will fetch such events to the attention of the relevant staff. United Utilities and the University of Exeter have designed algorithms to assume consumer water demand 24-hours prior to forecasting with 98% accurateness with the consequence of minimizing pump usage and therefore energy depletion.

Conclusive Defense to Water Crisis is Data Tools

Since the era of big data is prevalent, water supply businesses will be able to use next-gen sensors that will seize previously hidden changes in substructure performance. These forecasting technologies will help companies expect equipment problems and leakages. Smart technologies can help water suppliers enhance their customer service. For instance, information and logical system with a self-service function using an improved method of recording and assessing water quality data could let users display and enhance their own water consumption. Data alone will not solve the global water crisis, but the work of groundbreaking new companies is as wanted as the investment they are getting. Technology has the potential to solve some of the biggest global problems, and water shortage is one of the chief problems, in which data could have a real effect, given its ability to reduce unwanted and improve efficiency.

Data Tools Alone won’t make a Difference

It is significant to remember, although when talking about water scarcity that the problem is not narrowed to these fewer economically developing countries. Flint, Michigan’s contemporary water crisis is an instance of poor water resources brought about by corporate inability in an economically authoritative country, rather than a lack of supply. We have to consider the human inputs along with the assistance of data tools to fight back. Remember, data tools alone won’t make a difference. Hovering awareness of and contending poor water supplies is not easy, but there are numerous companies looking to utilize the power of data tools to lead to substantial change. Water shortage is as frequently financial or political as it is geographical, and the use of data tools in more efficiently sourcing water, could help to solve the less. The good news is that Silicon Valley’s role in availing water solutions is rising and the profitable market for these companies is far more painful than many might think.

