Auto-Calibration of Global Flood Forecasting Systems using Artificial Intelligence
Lead Participant:
AMBIENTAL TECHNICAL SOLUTIONS LIMITED
Abstract
Floods are the most common natural disaster and the leading cause of natural disaster fatalities worldwide. There were 539,811 deaths (range: 510,941 to 568,680), 361,974 injuries and 2,821,895,005 people affected by floods between 1980 and 2009 (Source: PLOS). Globally, economic losses due to flooding increased from roughly US$7 billion per year in the 1980s to US$24 billion per year in 2001-11 (adjusted for inflation).
Flood forecasting is one of the most challenging and difficult problems in hydrology. It is also one of the most important problems in hydrology due to its critical contribution in reducing economic and human damages. In many regions of the world, flood forecasting is one of the few feasible options to manage floods. Reliability of forecasts has increased in recent years due to the integration of meteorological and hydrological modelling capabilities, improvements in data collection through satellite observations, and advancements in knowledge and algorithms for analysis and communication of uncertainties. However, scalability of flood forecasting technologies is still limited by the time and resources it takes to calibrate (or tune) parameters in hydrological models such that the forecasts are accurate.
This project aims to address this question of scalability by utilising the recent advancements in AI and Machine Learning along with the wealth of data now available to automate the process of calibration.
We will use the probabilistic programming approach which enables data, expert human knowledge and machine learning to work together to provide optimal, rigorous and reproducible automatic calibrations.
Achieving an automatic Machine Learning calibration of hydrological models would remove the current bottleneck, enabling faster and more accurate flood forecasts. This will permit more flood forecasts over wider areas, all of which leads to less economic damage and a reduction in the risk and negative impact that flooding can bring to peoples' lives.
Flood forecasting is one of the most challenging and difficult problems in hydrology. It is also one of the most important problems in hydrology due to its critical contribution in reducing economic and human damages. In many regions of the world, flood forecasting is one of the few feasible options to manage floods. Reliability of forecasts has increased in recent years due to the integration of meteorological and hydrological modelling capabilities, improvements in data collection through satellite observations, and advancements in knowledge and algorithms for analysis and communication of uncertainties. However, scalability of flood forecasting technologies is still limited by the time and resources it takes to calibrate (or tune) parameters in hydrological models such that the forecasts are accurate.
This project aims to address this question of scalability by utilising the recent advancements in AI and Machine Learning along with the wealth of data now available to automate the process of calibration.
We will use the probabilistic programming approach which enables data, expert human knowledge and machine learning to work together to provide optimal, rigorous and reproducible automatic calibrations.
Achieving an automatic Machine Learning calibration of hydrological models would remove the current bottleneck, enabling faster and more accurate flood forecasts. This will permit more flood forecasts over wider areas, all of which leads to less economic damage and a reduction in the risk and negative impact that flooding can bring to peoples' lives.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
| AMBIENTAL TECHNICAL SOLUTIONS LIMITED | £170,580 | £ 119,406 |
Participant |
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| INNOVATE UK |
People |
ORCID iD |