Clean Technology Meets Data Science: News and Articles

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

When are machine learning and data science useful in the water sector? Are they useful if you have a large system with lots of data? Or are they useful if you’re looking at small-scale, decentralized systems?   The answer, as you might have guessed, is both. The difference is in the type of tools and algorithms being deployed and the results that are being sought - but, in both cases, machine learning and data science provide invaluable help in getting people access to clean, safe drinking water. Let’s take a look at two very different applications of water tech. One is for a water utility that is attempting to improve its systems to ensure that clean, safe water continues to flow to the citizens of the city and the other is for a small, rural community that needs cheap, reliable access to clean water. Interestingly, while the goals and requirements of both these applications are very different, the thread that connects both of them is machine learning. Today, we’ll talk about the chal

If you were thinking about countries and cities that were at the forefront of innovation in the water sector, would Singapore immediately come to mind? Singapore has long been researching and implementing methods to conserve water, to reuse water and to work with citizens and the community on understanding their needs - both now and in the future. In many ways, the challenges that Singapore faces are the challenges of the future - a small city with limited access to natural resources, including water; a high-tech economy that provides its citizens with the comforts and benefits associated with a developed country; and a changing climate that is impacting its ability to deliver those benefits. So, let’s talk about how Singapore is using the latest in data science and machine learning to help solve its water problems! Singapore’s Public Utilities Board (PUB) is the national water agency responsible for “supplying good water, reclaiming used water and taming stormwater”. As an agency, it

Last time, we looked at how models and data science are used in measuring, monitoring, predicting and responding to a changing climate in the latest IPCC reports. Today, let’s look at the results from the reports. First, we’ve now reached an average warming level of 1.1 0 C [0.95 0 C - 1.20 0 C] compared to pre-industrial levels (1850-1900) . This result is based on satellite and sensor observations taken from the land and the oceans. Further, when compared to paleoclimate data (for e.g. data from ice core samples existing millions of years ago), the key indicators of the climate system are increasingly at levels that have not been seen for centuries and are changing at rates that are unprecedented for the last 2000 years. Also, several studies have shown that the ocean absorbed a significant amount of heat between 1998-2012, a process that resulted in a smaller rate of increase in land surface temperatures. However, this effect appears to be temporary, with strong warming seen since 2

I t’s Earth Month and the Inter-governmental Panel on Climate Change (IPCC) just released the third installment in the series of reports on the state of the planet. The first report dealt with the science of climate change and the second one looked at its impacts on society and the planet. The third report was released a few days ago and looked at our response to climate change as societies and what we can expect as a result. IPCC reports have been released approximately every decade since 1990 - the current one is the 6th installment. Several hundred scientists collaborate on the technical and scientific assessments - reviewing the latest research published globally, combining multiple scientific areas and disciplines - in order to develop as comprehensive a picture as possible of the state of the planet. Just as an example, the second report on impacts to the planet draws from 34,000 studies and involved 270 authors from 67 countries.   The first report , released in December 2021, e

In the first of our two-part conversation on machine learning in climate science, we talked about the main challenges in using machine learning in earth and environmental science. Today, let’s talk about why we go through the effort of using these tools in clean technology when they require a significant investment in understanding and modifying them. Why use machine learning? Three main reasons - 1) Do it better 2) Do it faster 3) Find unexplained trends or patterns. 1) Do it better : Last time we talked about the challenges of using machine learning in solving problems in clean technology. And that’s still true for unmodified, off-the shelf models. However, there’s a huge opportunity for scientists and engineers who are interested in understanding and adapting these models to make them work effectively with all the other tools in the tool box!   Let’s look at one such adaptation where machine learning algorithms can be used in concert with physics based models to generate more accur

  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