Will machine learning help to protect water in cities?

Featured article by Paige Wright

Water is a unique natural resource that every country possesses throughout the earth. It is expected that you can get it in any country like kasyno z blikiem. Despite being the largest natural resource and covering 71% of the earth’s surface, only 1.2% of fresh water is drinkable. Because of this, drinkable water is rare and treasured as certain metals.

Freshwater acquired from streams and rivers can be utilized for several purposes, including industrial, domestic, commercial, and emergency. Due to this versatility and scarcity driven by climate change, freshwater is a point of contention for most countries as they expand in population. 

Therefore, to provide a stable water supply, urban flexibility, and good urban governance, it is indispensable for cities to have a healthy water infrastructure that can accommodate their citizens. What is the practice that can help protect water in cities? How can machine learning help protect waters in cities?

Building a good water distribution network

A critical component of the urban water infrastructure is creating a good water distribution network. This network system can handle the demand for drinkable water without any losses. Nowadays, water leakage is a major reason for water losses, especially in distribution networks. 

Water leakage occurs in several stages of the distribution process, including transmission, distribution, treatment, and storage. The leakage issues have been handled in several ways to a particular extent. The decreasing amount of non-revenue water can improve metropolises’ health, safety, environmental, and socioeconomic outcomes. 

An improved water leakage management system is required 

With improved technology and techniques, there is a need to abandon the current methods of managing water leakage. This becomes necessary for several reasons.

Firstly, the current method is not predictive but corrective. A predictive component provides predictive maintenance that involves pre-scheduling and is conducted manually. Besides this, it is also time-consuming. In addition, implementing corrective measures might require shutting down the systems that will affect citizens, especially those who require an endless water supply. 

Secondly, most corrective measures to handle water leakage are for larger leakage, whereas smaller leakages are unchecked for a long time. Because of this, smaller and unrestricted water leakages cause a high volume of water losses and revenue. 

How to solve the water problem using machine learning? 

With all these issues, there is a need to provide a permanent solution to help protect water in cities. A globally accepted solution to checkmate and control water leakage in cities depends on machine learning operating real-time sensor networks. Real-time sensor networks include components such as cloud storage, network monitoring, sensors, and supporting applications. 

The real-time sensor network can benefit cities by offering a more accurate prediction and complexity of water leakages. Secondly, it can also help provide a convenient and accurate leakage location. A shorter U-turn time when resolving any water leakage is critical. In addition, the subsequent efficacy in handling water leakages can minimize downtime to water supply systems by improving the quality of water and the dependability of water supply.