Posts

Predicting floods using models for Covid and other infectious diseases

  What do Covid-19, social networks, traffic congestion and floods have in common? On the face of it, the answer would seem to be - nothing. But interestingly enough, all these systems can be represented in similar fashions - as nodes and networks with spatial and temporal effects.   So, scientists and researchers often try to see if models and ideas developed to solve problems in fields with similar representations can be adapted to problems in other fields.   A really interesting study was published in Nature   a couple of months ago, where researchers explored what happened when they adapted models used in understanding how infectious diseases like Covid spread to predicting floods in cities and urban systems.   How have floods and their impacts typically been modeled and why do people care? To start with, people care about flooding because it directly impacts their lives - when do areas have to be evacuated, how long should the evacuation last, and what are potential health consequ

Oceans, Ships and Data Science

  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 wate

Moonshots, Models, IoT and Machine Learning in Agriculture

  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

Innovation In The Water Sector

  I was at   Imagine H2O’s “Water Innovation Week” conference   this week - virtually, of course! Imagine H2O is a wonderful resource and accelerator for startups in the water space, and their program this week was an excellent representation of water’s central role, not just in our daily lives, but also in the clean technology sector in general.     In most of the developed world, water isn’t really at the forefront of most people’s minds. Turn on the tap, you get clean, free flowing water - and unless you’re in the water sector, you’re probably not thinking about things like aging water infrastructure, budgets and how to fund water infrastructure, how to ensure that water is used efficiently, that wastewater is effectively treated and that tradeoffs between the water allocated to different sectors are discussed and managed equitably. In fact, unless there’s a storm or a flood or a leak in the water pipes in your house - water is not your primary concern.   And that is how it should b

Agtech, Farmtech, Foodtech, Livestock tech - the market for agriculture and data science over the last decade

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  It’s always interesting to take a look at how trends and predictions about new technologies and their ramifications for different sectors pan out. And that’s no different when it come to data science and clean technology.   A graph that often comes up when discussing how new technologies develop is the “Gartner cycle of hype”. This is the idea that all new ideas, concepts and technologies invariably go through several stages in their development - they all start with excitement as the promise of new technology opens up possibilities that seem limitless, followed by a crash course in reality when what’s actually possible collides with dreams, and finally a steady look at immediate solutions that can build towards the dream. The last stage is when startups gain traction or are acquired and larger companies start building teams to work with the new technology. So, how has agriculture and data science - or AgTech worked out?     It’s definitely been an interesting ride since the concept

Building Models With Missing Data

  Have you ever worked with a real-world problem where you have all the data that you need in a form that you could easily use to build models?   In the case of most problems, we find that data are missing, or there are errors in how the data are measured, or we’re faced with different types of data that need to be integrated. That’s been especially true in many clean technology fields - water, energy, climate, sustainability, ecosystem restoration and agriculture among them.   So, how do we deal with data with so many challenges?     One way is to see if there are alternative ways of measuring the data. One possibility is to identify surrogate datasets that can be calibrated and used as alternatives for the primary measurement. A second possibility is using cheaper, more widely distributed sensor data such as Purple Air sensors for air quality monitoring in combination with the primary data sources so that models can be developed. A third alternative is to use modeling techniques like

Coming this Sunday, September 20th: Bayesian networks in clean technology live, virtual workshop

  How do you find out why new technologies are being adopted? How do you find the early adopters and figure out why they are using these new technologies?   As startups and individuals build new tools and applications in agriculture, water, energy, sustainability, forestry and climate - some of the the biggest questions they face are understanding who is likely to adopt these technologies, the parameters governing these decisions and how they interact with each other.     So, how can this be measured and modeled quantitatively? Welcome to the wonderful world of Bayesian networks!     Bayesian networks are powerful machine learning algorithms that allow us to model how different aspects of a problem are interacting with each other, estimate how likely it is that someone will choose to do something like buy a new technology, account for the uncertainty inherent in problems in clean technology where we don’t know all the parameters and values associated with them - and solve a whole suite