In partnership with Norwegian company Think Outside the team developed a solution to forecast the water availability coming from rivers and snowmelt into reservoir lakes as well as electricity prices to optimize the renewable energy production in Scandinavia.
Think Outside is a Norwegian company currently focused on providing clients with “constant access to accurate and reliable snow data … to make projections you can be confident in”.
They use radar systems to image snowbanks so that they can provide their clients within the hydropower energy industry with data about the density of snowpacks. The hydropower companies can use this data to make better predictions about the future volume of water that will be flowing into reservoirs due to snow melting and from this make more accurate predictions about the amount of energy they will be able to generate from this water as it passes into reservoirs and then through the hydropower electricity turbines.
Think-Outside wished to expand its current data and forecasting offerings so that they are able to provide additional value to its clients in the hydropower industry. The goal of this project was the development of a machine learning pipeline to make predictions for both water inflow into reservoirs/lakes and future electricity prices.
The project outcomes
Due to the different requirements of water inflow and electricity price modeling, the project was divided into two sub-projects: (1) Water inflow prediction and (2) Electricity price prediction. For both sub-projects, we defined the task objectives relating to the project goals specified by Think-Outside, explored numerous data sources, downloaded and processed data (in an automated manner where possible), and then cleaned, explored, and preprocessed the data.
Finally, machine learning models we created and trained on the input datasets and then these models could be used to make predictions about future values of water inflow on a per reservoir/catchment area level and also to predict future values of electricity prices of different energy bidding zones within Norway. The performance of the machine learning models and predictions was analyzed using a number of different error metrics as well as visual representations of the model performances.
The team developed a data visualization dashboard, which shows and compares regenerative farming practices and their impact on Soil organic carbon (SOC) using historical data. The dashboard functions as a tool to analyse and develop pathways to predict SOC.
The EU is pursuing ambitious policies to combat climate change. It requires the neutrality of carbon emissions in all sectors. The agricultural industry needs a transition toward a more sustainable model, and the rest of the sectors apply restrictions regarding the activities that emit CO2.
First, farmers are both the cause and the affected part of climate change, the loss of biodiversity, and soil erosion. These impacts are significantly affecting productive activity, especially in Spain. Two major barriers to the transition to a regenerative agriculture model would be the solution: the lack of knowledge on how to make the transition and the lack of financial resources to carry out said transition safely and progressively.
Second, companies need by legal imperative, for competitiveness, and for CSR reasons, to achieve neutrality of emissions. This represents a significant investment, and companies can be accused of doing green-washing if the carbon credits they compensate are not duly certified. For this reason, it is necessary to ensure that the carbon capture is accurately measured. Furthermore, they have to align their activities with the SDGs.
Third, current carbon standards and certification bodies require accurate monitoring of carbon sequestration and soil organic carbon (SOC). Agriculture has a high uptake potential, but monitoring costs are very high, making the process unprofitable. Increasing soil organic carbon (SOC) content is crucial for soil quality and climate change mitigation.
The project partner provided various datasets such as their farming data, SigPac, and WISE-3. The team collected additional data from the ICGC website and the LUCAS topsoil dataset, a European survey collected every three years since 2006.
The project outcomes
We developed and deployed an online data visualization application. The dashboard includes various machine learning models to predict SOC.
Grasslands, Shrublands, and forests represent the highest amount of SOC.
These can alter the prediction results for the agricultural farms, hence they need to be excluded.
Screenshot 1 of dashboard visualization
From the below images it’s feasible that Biomass gets reduced in 2021 apart from the Western Region of Catalunya experiencing HighBiomass.
Within two months, the team built two risk scoring systems, using Analytic Hierarchy Process (AHP) models for predicting climate and geopolitical risks to help financing institutes make more informed decisions.
Finz focuses to help global decision-makers, such as government entities, banks, or foundations, to finance autonomous and light units for water or energy efficient accesses, in urban and rural contexts. It is now widely accepted that climate change or geopolitical issues pose serious threats to the availability of essential-to-life resources, such as water or energy.
In emerging countries (Africa or Southeast Asia), access to resources for human or animal consumption, and agriculture needs to secure food, are keys. In developed countries, climate change threatens the availability of water (e.g. California) and will figure a big issue for all inhabitants on Earth tomorrow.
We believe to help how decision-makers act and decide with accuracy in predicting risks of damage /destruction on assets or population that could be a catalyst factor to adjust essential-to-life needs with decisions.
The Project Results
More than 40 technology change-makers worked on publicly available datasets, like satellite imagery, land cover data, and news outlets, to Extract relevant data points, Transform these points into useful information sources, and Load these in newly created models to compute Climate Change or Geopolitical driven Risk Scores.
50 AI engineers collaborated for 8 weeks to analyze sensor data and test possible systemic data models to develop an intelligent recognition algorithm to detect fire of different types of wood.
The project partner, Dryad Networks, is a Germany-based startup that provides ultra-early detection of wildfires as well as health and growth-monitoring of forests using solar-powered gas sensors in a large-scale IoT sensor network. Dryad aims to tackle wildfires, which are causing up to 20% of global CO2 emissions and have a devastating impact on biodiversity.
The world’s forests are burning! If current trends continue, up to 170 million hectares could be lost until 2030 and with it, we gradually lose the earth’s great carbon sink consuming 110 billion metric tons of CO2.
The project outcomes
The project’s goal was to build an intelligent model that will detect fire of different types of wood through analysis of the existing sensor data, thereby enabling alarms for firefighters early enough, so they can extinguish it. During the period of eight (8) weeks, the team designed and implemented several data-based pipelines, leveraging the dataset provided by the Dryad team. Combing a massive and extensive analysis of the datasets provided, together with the state-of-the-art machine learning techniques, the team delivered the following.
The results of this project lie in the state of art machine learning models and correctly classify the sensor data into two categories, “in-smoke” and “clean-air”. The model developed in this project is scalable and replicable. Such a solution has the potential to reduce forest fires, thereby enabling alarms for firefighters early enough, so they can extinguish them. This will ultimately help to achieve the sustainable development goals in the areas of life on land and climate action.
With a global of 50 AI change makers, the team will develop a model for 12-hour rainfall forecasting to help vulnerable communities and children plan and mitigate adverse weather conditions such as drought, floods, and storms in both the short and long term, in this high-impact 8-weeks challenge.
Small-holder farmers in West Africa are very sensitive to rainfall and flooding events. Yet they do not have access to weather information, and furthermore, thunderstorm forecasts are poor, because satellites do not have the resolution to accurately predict these relatively small-scale weather features.
The Kanda Weather Group has a working IoT product that collects upper air data using a weather balloon at a very low cost and sends the data back to the ground receiver. We are also completing a weather app that can show a forecast in a dashboard format. We have been testing these weather balloon (also called radiosonde) launches at two universities in West Africa.
Our most recent work has been to create an initial machine learning model using precipitation data to make a 12-hour rain forecast for the 500 square kilometer region that initiated the launch. Now, we would like to utilize a more complex ML or AI method to achieve the same outcome.
The Project Goals
Develop a model that forecasts 12-hour rainfall at a skill level better than the previous model and better than climatology. The training datasets to be used are (6000+) radiosonde weather balloon launches from National Weather Stations located in the United States that are cross-referenced to corresponding rainfall data 12 hours in the future for the same location. The creation of a model of this type implies that a good 12-hour rainfall forecast can be made after sending up a single weather balloon in the morning.
Future work includes further atmospheric modeling, including fire weather over California or air quality forecasts in polluted regions.
Why join? The uniqueness of Omdena AI Challenges
A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.
And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.