What comprises the water sector? If you asked ten people working in the sector, you’d probably get ten different opinions! And that’s because there are so many aspects of working with water and water technology.
There’s drinking water - which involves figuring out how much water is available (water resources management), how to treat it, how to deliver it effectively and at an acceptable price to customers. Then there’s wastewater - how do you treat, remove and recycle wastewater, both residential and industrial, to acceptable levels. Next, we come to the interactions between water and other built systems - hydropower and irrigation being the largest. Then, there’s the impact of water-related disasters - floods and tsunamis for example. Finally, we’ve got natural resources management - where we explore, monitor and evaluate the condition of natural water bodies - lakes, rivers, glaciers and the ocean - and how they interact with other systems.
The fun part about working with water is that there are so many different ways that machine learning, robotics and data science in general can be applied used to solve problems. For example, let’s take a look at some of the applications that are being deployed in the ocean.
While there are many applications that look at what’s happening on the coast - monitoring tsuanmis, identifying and monitoring water pollution, modeling sea-levels and their impacts on coastal communities, siting and building offshore windmills - there are also a whole suite of applications associated with the ocean itself. And many of the these applications involve using machine learning, remote sensing and other data science tools.
The most important of these applications is tracking the movement of ships on the ocean. This is necessary for understanding economic activity, monitoring the status of ports, identifying security threats on the oceans, monitoring fishing activity and overfishing and understanding ocean conditions. With Google Maps, it’s pretty easy to navigate our cities - but what happens with ships in ocean? Ocean shipping forms an integral component of our economic activity and figuring out the movement of ships and tankers is essential to understanding and monitoring shipping activity. So, how exactly is that done? It’s done in a few different ways - first, using high resolution satellite data with image recognition algorithms that can identify ships in ocean imagery. Second, using radar data and the unique identification number of a ship to track it’s movement through the oceans. Third, using a combination of video surveillance, buoy sensors and other sensors to monitor what’s happening at ports and identifying changes.
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
As the impacts of climate change on the planet become clearer, scientists and professionals in climate science are looking at the latest tools and technologies in AI and machine learning to help understand and mitigate the effects. At the same time, career opportunities in the field are growing and we’re seeing increasing numbers of students and early career professionals interested in developing and using their skills in ways that can help the planet. So, when and where can machine learning and AI be used in climate science? And what are the pitfalls? If you’re working in environmental and earth sciences, you probably already have a pretty big toolbox that has been developed over several decades! It consists of standard statistical techniques including spatial and temporal statistics, a range of physics-based or process based models, and several data collection and data integration technologies at different scales. What can machine learning add to this? Does it replace all the o
Last time we looked at how machine learning can help water utilities manage their maintenance and operations efforts - especially when dealing with hard-to-reach parts of the water system like buried water pipes. Today, let’s talk about how machine learning is being used in developing new technologies and building prototypes for decentralized, small-scale systems. Desalination has been studied and deployed at scale for several years now. As different parts of the planet face increasing water stress, desalination is being evaluated as one of several potential solutions - together with water conservation and recycled water. In the Middle East of course, large-scale desalination plants have been in operation for several decades, with Israel being one of the countries at the forefront of developing and implementing the technology. Large-scale desalination plants have the advantage of scale - you can build a single system and then connect it to your existing water network. Sufficient data a