Clean Technology Meets Data Science: News and Articles

What are some of the interesting startups in the water sector this year? Water tech typically does not get funded at the same rate as other clean technology sectors - primarily because the time to achieve an acceptable ROI for venture funds is usually longer than the lifecycle of the fund. However, there are still VCs who are interested in funding the water sector and there are always startups that find government funding, partnerships and other ways to build and grow. Let’s take a look at some startups that are combining data science and water to help solve interesting problems! 1. Ainwater , headquartered out of Chile, uses AI-based algorithms to optimize water and wastewater plant operations, ensuring compliance while increasing energy efficiency by 30%. 2. CivilGrid , in the San Francisco Bay Area, helps collect all the geospatial and regulatory information required for water companies and other infrastructure companies to plan their operations. 3. IFlux , out of Belgium, uses a co...

In April, the IPCC published the next installment of reports on the state of the planet . Several alarms on the state of the planet were sounded - and yet, most researchers were concerned that the report did not go far enough. Why was that? One of the challenges with synthesizing science across multiple areas and bringing it to the point where policy makers can make decisions that follow the science is that the latest research often doesn’t make it into these reports. That’s because the research is cutting-edge, which by definition means that new discoveries are happening and the science is not yet at the point where clear recommendations can be made. Second, with cutting-edge science, researchers are still discussing the nitty-gritty details of what the processes are, what’s driving them and how the system is functioning. That makes it much more difficult to reach a scientific consensus on the topic.   So, what is one such area of cutting edge research in climate science? Climate ...

It's the end of 2022 and we have another few weeks left before our cohort finishes the course on "Introduction to Machine Learning and Data Science in Water and Wastewater". We've had so many amazing people take this course and discover new insights into what's happening with the data at their water utility or organization. As we've been building our courses for individuals, we've learnt so much about what is useful to utilities and people working in the space.  So for 2023, we are going to be taking a short 4-month hiatus where we are focusing on building some new and amazing courses that will help professionals in the space learn more of these skills. We're always looking for feedback, so please do email us while we are building our new courses!

 We had such a terrific response to our first ever  " Introduction to Machine Learning and Data Science in Water "  course, that we've decided to open it up for registration for the second time. If you're in the water sector, are curious about machine learning and data science and not sure how to get started using it in your work - join our course and we'll give you the tools! This is the second time we're doing a 10-week, cohort-based course and we plan on doing more of them in the future. So, what does our asynchronous, cohort-based course do for you? One of the challenges with completing courses online is that it's so easy to sign up for one, get busy with life and work, fall behind and then give up on finishing the course that you signed up for. Our cohort-based and asynchronous approach helps you overcome that. The course material is online - so you get to complete it and review it when you get the time. However, we also have weekly office hours that ...

  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 c...

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !

 We're delighted to welcome our first ever cohort to our " Introduction to Machine Learning and Data Science in Water " course. If you're in the water sector, are curious about machine learning and data science and not sure how to get started using it in your work - join our course and we'll give you the tools! This is the first time we're doing a 10-week, cohort-based course and we plan on doing more of them in the future. So, what does our asynchronous, cohort-based course do for you? One of the challenges with completing courses online is that it's so easy to sign up for one, get busy with life and work, fall behind and then give up on finishing the course that you signed up for. Our cohort-based and asynchronous approach helps you overcome that. The course material is online - so you get to complete it and review it when you get the time. However, we also have weekly office hours that are live - so you get to ask your questions to our experts, have th...

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 ...

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 202...