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Artificial intelligence in the face of climate change: How it will help us prevent floods

Artificial intelligence in the face of climate change: How it will help us prevent floods

  • Artificial intelligence is revolutionizing flood management, addressing the increasing intensity and frequency of these events attributed to climate change.
  • Its ability to analyze large volumes of data, such as satellite images and hydrological measurements, has significantly improved accuracy in flood prediction and management.
  • Notable examples of AI techniques include deep learning and neural networks, which identify complex patterns in extensive data to predict floods more accurately.
  • However, despite its advancements, AI faces challenges, primarily in the quality and availability of data.



Over the last decade, we've witnessed a significant rise in extreme weather events, with floods becoming a recurring and devastating phenomenon. This increase, largely attributed to climate change, is altering weather patterns and hydrological cycles, challenging traditional management and response capabilities.

In response, artificial intelligence (AI) has become an essential tool in flood risk management. Its ability to process large volumes of data, from satellite imagery and hydrological measurements, enables a deeper and more accurate understanding of flood patterns. AI's improved prediction accuracy and faster emergency responses are crucial for urban planning and disaster preparedness, protecting lives and property in a climate-changing world.

Examples of AI Techniques Used: Deep Learning and Neural Networks

In the field of artificial intelligence (AI) applied to flood prediction, techniques such as deep learning and neural networks have proven particularly effective. These methodologies fall within the broader scope of machine learning (ML), a subfield of AI that allows machines to automatically identify patterns in complex data sets without explicit programming.

Deep learning employs multi-layer neural networks to analyze and learn from large data sets. This ability enables the identification of complex and subtle patterns, which can more accurately predict when and where floods may occur.

As explained in the comprehensive article from the "United Nations Platform for Space-based Information for Disaster Management and Emergency Response," machine learning is primarily classified into two categories: supervised and unsupervised learning. Supervised learning uses labeled data to predict future events, based on known examples, while unsupervised learning works with unlabeled data, finding structure within these data on its own. In the context of floods, these approaches allow for a deeper understanding of hydrological and meteorological patterns, crucial for effective flood prediction and management.

A specific example of the application of these techniques in flood prediction is the use of radar and satellite images to map and monitor floods. For instance, the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and medium-resolution sensors like Landsat and Sentinel-2 for identifying flooded areas. However, conventional remote sensing methods have limitations, such as errors in water classification and cloud shadowing, leading to overestimation or underestimation of flooded areas.

In this context, machine learning offers significant advantages. For example, physically-based models, long used to predict hydrological phenomena, often require intensive computation and a variety of hydro-geomorphological tracking data, which limits their ability to predict short-term floods. Machine learning algorithms, on the other hand, can effectively handle large volumes of data from various sources and provide faster and more accurate predictions.

A notable study in this area is the Sen1Floods11 project, which provided a dataset of surface waters to train, validate, and test Fully Convolutional Neural Networks (FCNNs) for flood water segmentation. This dataset included 4,831 chips covering flood events on six continents, demonstrating the global applicability of these models.

Additionally, machine learning has been used to estimate urban flood damage using the coherence loss of interferometric signals from SAR sensors like Sentinel. This application highlights how ML can overcome challenges presented by traditional threshold-based methods, offering a more flexible and precise approach.

Despite the numerous advantages, machine learning algorithms have limitations. The quality of system training and the selection of appropriate algorithms are crucial to their success. Furthermore, the accuracy of the ML algorithm used largely depends on the tuning parameters, and choosing the most effective algorithm can be a complex process.

In conclusion, machine learning, especially deep learning and neural networks, offers an innovative and efficient approach in flood prediction and management, overcoming the limitations of conventional methods and opening new possibilities for water-related disaster mitigation.

Success Stories and Practical Applications

In flood management, the practical application of artificial intelligence (AI) has demonstrated remarkable results in various regions of the world. These success stories highlight how AI, applied in different contexts and challenges, is significantly contributing to flood prevention and response. From global projects to local initiatives, each example provides a unique perspective on the effectiveness and potential of AI in this critical field.

Hurricane Harvey in Texas 

An impactful example of the practical application of AI can be found in the context of Hurricane Harvey in Texas. This devastating event, one of the costliest hurricanes in U.S. history, highlighted the growing risks of flooding and the limitations of existing flood maps. The lack of flood risk perception led to fatal consequences during Harvey, especially when people attempted to cross flooded roads.

Facing these challenges, Amir Behzadan and his team at Texas A&M University proposed an innovative solution using AI: estimating the depth of floods from photographs of submerged stop signs. This technique, which compares the visible portion of the submerged signpost with its total length in a flood-free state, is complemented by the BluPix application. This application, funded by a NOAA research grant, allows users to contribute to flood data collection by uploading photos of submerged stop signs, which are then used by the AI model to assess the depth of flooding. This approach not only enhances community awareness but also informs the development of flood mitigation policies.

Google AI Flood Forecasting

 A Global Impact Solution Google's flood forecasting project, using AI, represents a significant advancement in global flood prediction. This project leverages AI algorithms to analyze hydrological and meteorological data, providing accurate and early flood alerts. Its global approach allows regions with limited resources in flood prediction to enhance their preparedness and response capabilities, highlighting the potential of AI to offer solutions with significant impact in vulnerable communities.

Flood Prediction in Cork

In Cork, Ireland, an innovative artificial intelligence (AI) model is being used to improve accuracy in flood prediction and associated risk management. Developed by researchers at CeADAR in University College Dublin, this model uses satellite data to map historical flood events and predict future floods with remarkable precision. This advancement provides local authorities and communities with crucial tools for emergency measures, damage limitation, and better preparation for extreme weather events. The ability to accurately predict the extent of future floods is especially valuable in the context of climate change, which is expected to intensify the frequency and intensity of these natural disasters.

GAO Report - AI in Flood Management in the U.S.

report from the U.S. Government highlights the increasing use of Artificial Intelligence (AI) in modeling natural disasters, including severe storms, hurricanes, floods, and wildfires. This study, published by the Government Accountability Office (GAO), examines how machine learning, a form of AI, can improve the accuracy of forecast models, increase warning times, and reduce uncertainty. Although some machine learning models are already operational, there are challenges to their further development and wider adoption, such as the lack of data in rural areas to train the models.

UNO Research on AI for Flood Control

At the University of New Orleans, researchers are using artificial intelligence to assess and detect deficiencies in the flood control structures in the United States. The project, funded by the U.S. Army Corps of Engineers, uses images from unmanned aerial systems and other sensory data to examine the integrity and stability of flood control systems. This research is crucial for safety and infrastructure, as the New Orleans region faces flood risks from the Mississippi River, rainfall, and storm surges.

Challenges and Future of AI in Flood Management

The application of artificial intelligence (AI) in flood management, while having made significant progress, faces considerable challenges. The Stockholm International Peace Research Institute (SIPRI) report "Artificial Intelligence for Climate Security: Possibilities and Challenges" underscores these challenges and the opportunities that AI presents to address them.

One of the main challenges is the quality and availability of data, which are essential for the performance of AI models. This problem highlights the urgent need to develop hybrid models that integrate different approaches and types of data to improve the accuracy and utility of predictions. Adapting to and mitigating climate change through AI requires an interdisciplinary approach that incorporates advanced technologies and new sources of data, such as IoT sensors and more sophisticated satellite image analysis.

Looking to the future, the potential of AI in flood management is immense. Technological advancements and the integration of new data sources are expected to lead to more robust and effective solutions. This could result in more efficient management of water resources and faster, more accurate responses to flood-related emergencies. The ongoing adaptation of AI to changing climate dynamics will be crucial for effectively addressing flood risks in the future.

The SIPRI report also points out that AI can play a vital role in creating more effective adaptation and mitigation strategies against climate change, resulting in more resilient communities and protected ecosystems. However, to overcome current challenges and fully leverage the capabilities of AI, the development of advanced technologies and the integration of interdisciplinary approaches are key.

In a context of uncertainty about the impact of artificial intelligence development on our society, we can say that its unstoppable progress opens promising paths for more efficient and effective management of flood-related challenges in an increasingly climate-affected world.

Source : https://smartwatermagazine-com.cdn.ampproject.org/c/s/smartwatermagazine.com/news/smart-water-magazine/artificial-intelligence-face-climate-change-how-it-will-help-us-prevent?amp


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