Ambiental is developing innovative flood forecasting and flood monitoring technologies in order to tackle urban challenges. They are currently conducting a project for London, building decision support systems which combine flood risk insight and Artificial Intelligence (AI) to enable greater resilience in the face of growing environmental challenges.
The bulk of the project involves deploying their proprietary FloodWatch® flood forecasting technology, to give advanced warning of floods in Greater London. Sudden, high intensity storms, resulting in flash flooding can cause major, unexpected impacts on the nation’s capital city in the form of risk to life, damage to property, and severe impacts for transport and critical infrastructure.
Ambiental’s modelling software can predict flood events hours in advance through continuously processing Met Office rainfall forecast data feeds. This model simulates the entire hydrological system by calculating hydrological flows and then hydraulically modelling flood evolution.
The objective of the 9-month project is to develop a flood data delivery system dashboard. Through the integration of multiple data sources, including real time sensor telemetry and big data, the aim is to provide improved, actionable intelligence to our government stakeholders. Serving this data via a common application interface unlocks the information’s true power. The new EnviroTracker™ smart city dashboard front end application concept is designed to enable city authorities to manage and visualise high resolution flood forecasting footprints.
The initiative is funded under the UK Space Agency’s (UKSA) Space for Smarter Government Programme (SSGP). Ambiental’s data driven software application aims to deliver geospatial insights, which in turn, lead to positive benefits for the UK Government. As part of its activities, SSGP provides funding for research and development of applications, with the intention to increase uptake of satellite and Earth Observation (EO) data within the public-sector.
Ambiental’s FloodWatch product is a newly developed flash flood forecasting system, which is currently being used in Malaysia and is now being deployed in London. Their work aims to mitigate the effects of sudden flash flooding, which in the past has severely impacted cities like London with little or no warning. Key stakeholders for the project include Transport for London and the Greater London Authority.
The Environment Agency has estimated that 140,000 people in London are at high risk of flooding, whilst 230,000 are at medium risk. Studies have shown that almost a third of all London Underground stations have significant or high flood risk. As a result, any alerting of imminent flood dangers will improve emergency response, which in turn can reduce danger and potentially lower financial impacts.
This exciting initiative also explores the value in using big data, specifically social media, to inform decision-making around flood incident management. It is notoriously difficult to predict rainfall generated (pluvial) flooding accurately because it can occur anywhere. Artificial Intelligence (AI) can deliver improvements by providing ordered reports of flooding which serve as model validation and support actionable intelligence workflows. A key component of the project will be to assess techniques for interpreting Twitter posts which mention floods. This will provide a form of validation for improving the skill of subsequent flood model iterations. Furthermore, the project tracks land-use alterations, specifically changes in green space. This data can be fed back into the dynamic modelling environment and used to maintain the accuracy and currency of the flood forecasts produced.
Ambiental (www.ambientalrisk.com) is a global specialist in flood modelling, flood risk and climate change / natural hazard analytics. The company produces flood maps, flood data sets, flood catastrophe models, flood forecasting SaaS products and environmental reports internationally, and conducts both rapid, national-scale assessments and in-depth research that support better decision-making around flood risk management.