Data Science in water utility industry

utilize SCADA data and monitoring in real time
Water utilities have real time SCADA data in all the water plants and facilities. It is challenging to get the large amount SCADA data and make use of them.

• forecasting chemical concentrations in water in advance
  For water quality, water engineers should know how chemicals concentrations in water are changing. By knowing how the concentrations are changing, they can make right decisions on what to do to make water quality good by optimizing their operations of water quality. For example, by knowing chlorine, nitrite concentrations in advance they can decide when to flush the water pipelines again next. By doing flushing in right time just right amount, they can save costs and labors as well as efficiently operating water quality.
• optimize asset spending allocations to spend using machine learning throughout given years. in regulated water utilities, budgets are decided every 3 years by investors. Given budgets and assets should be well allocated to spend so that the given budgets and assets are all well used when the 3 years end. It should not be spent too little or too much. By using machine learning, the optimal asset spending allocation is efficiently decided. Based on the information, project managers and general managers can spend right amount budgets in each month and hit the target in earning test when the 3 years end.
• forecasting water supply is for predicting how much water should be purchased for shortage of water or drought.
  In Southern California, it is very important to forecast how much water will be supplied by producing the water because oftentimes, drought comes. If too little water is supplied, water utilities company purchase the shortage from different areas' water utilities companies. To set up the budget and purchase right amount of water, it is very important to forecast water supply by water production of the water utilities company. By applying machine learning, the water supply can be forecasted.
• by forecasting water depth change in wells, engineers can know which wells will dry out by drought. By applying machine learning, the water depth of the wells are forecasted and the wells that will be dry are identified.