Snippets in Clean Technology and Data Science: Water
Today, let’s look at some of the ways in which sensors, machine learning and other data science tools are helping solve problems in the water sector
Water conservationis one of the largest components of the water sector. And with the droughts hitting California, New South Wales and South Africa recently, there’s been a lot of focus on education and changing people’s behavior through information. So how do we make it easy for people to do their best to minimize water use?Companies and organizations have tried several different approaches – public education programs about water use being one such example. Another being the water comparison report that water companies in California started sending out to their customers with the monthly bill. It’s a short summary that shows how your water usage compares with your neighbors and how much water you have used against your allocation. This is a fairly straightforward use of data science – it uses a nearest neighbor approach with a built in radius and constraints around house size and household type and compares aggregated water use. In other words, each individual house is compared against an average value – so privacy is protected.
Researchers in Germany decided to start using theInternet of Things (IoT)to provide real-time feedback and see how that compared with people’s behavior after receiving reports. They built sensors that monitored water use, water temperature and energy use and placed a digital display so that it would be visible while showering.What they found was that having this information readily available resulted in people changing their behavior to conserve more water and energy – so much that some of the highest water users actually reduced their usage by 22%.On the other hand, providing the information about the water and energy use after the shower was taken had no appreciable effect on people’s behavior. And not just that – people didn’t get tired and stop performing the action after using the sensors for two months – the effects were as pronounced at the end of the two month period as they were at the beginning.
Water monitoringis another billion dollar market that spans several sectors. Let’s take a look at how the government has been harnessing the power of data science to improve water quality in the United States.
Monitoring water, whether in streams or the ocean, requires a lot of sensors. Traditionally, the data collected by these sensors has been downloaded manually and then analyzed. Also, as sensors are expensive, scientists usually place sensors at locations where there is the most risk – for example, where algal blooms are the highest or where there is a possibility that lead in the ground water might reach houses with kids.
As hardware becomes cheaper and software more powerful, we start getting “smart sensors” or to use the latest buzzword – the Internet of Things. At theChesapeake Bay , a small number of smart sensors have been deployed to monitor weather, water conditions and water quality. These sensors collect data, transmit them wirelessly to the cloud where baseline analyses are done and the results presented in dashboards. The data can be followed in real-time, making these sensors invaluable for several businesses – recreational boating, fishing and education among others.
Even though the sensors deployed in the Bay are “smart”, they still only monitor localized conditions – that is, conditions near the sensor. Using these to monitor the whole Bay would be prohibitively expensive at this point in time! So,scientistshave started working on ways to supplement the data collected by the smart sensors with remote sensing data – data collected by aircraft or satellites. Remote sensing data may not be as accurate as the sensor data, but covers a much broader area and is thus a much more viable alternative for monitoring large water bodies. This makes it easier for policy makers, companies and non-profits to make decisions about the health and status of large lakes, streams and rivers.
Even more interesting things happen when different types of data are combined – but that work is just getting underway! If you’re someone interested in how data science can be used for the public good, the water sector is definitely one of the ones to watch.
What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems? Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively - scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
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