Introduction
Floods disproportionately affect developing countries with few flow measurement networks, highlighting the need for accurate early warnings. The acceleration of flood-related disasters due to climate change underscores the urgency of effective early warning systems, especially in low- and middle-income countries where 90% of vulnerable populations reside. According to the World Bank, improving flood early warning systems in developing countries to bring them up to developed standards could save an average of 23,000 lives a year. However, challenges remain, including the need to calibrate each watershed and make limited forecasts in vulnerable regions. In this article, we will understand Google's research paper exploring the use of artificial intelligence (ai) to predict river flooding up to 5 days in advance, detailing its potential implications for over 80 countries, particularly in vulnerable and water-scarce regions. data.
The devastating impact of floods
Floods are the most common type of natural disaster, and the rate of flood-related disasters has more than doubled since 2000. This increase is attributed to the acceleration of the hydrological cycle caused by anthropogenic climate change. The impacts of flooding are particularly severe in developing countries, where populations are highly vulnerable to flood risks. The devastating consequences of flooding underline the urgent need for accurate and timely flood warnings to mitigate the impact on human lives and property.
The current state of flood forecasting
The current state of flood prediction faces challenges, particularly in ungauged basins where hydrological prediction models need more reliable data for calibration. This limitation hinders the accuracy and lead time of flood forecasts, especially in areas vulnerable to the human impacts of flooding. The lack of dense stream gauge networks in developing countries further exacerbates the inaccuracy of flood warnings, highlighting the critical need to improve global access to reliable flood forecasts.
<h3 class="wp-block-heading" id="h-a-ray-of-hope-google-ai-to-the-rescue”>A ray of hope: Google ai to the rescue
technology/ai/google-ai-global-flood-forecasting/” target=”_blank” rel=”noreferrer noopener nofollow”>Google artificial intelligence (ai) presents a promising solution to the challenges of global flood forecasting. By leveraging ai and open data sets, there is potential to significantly improve the accuracy, recall and turnaround time of short-term forecasts of extreme river events. The development of an operating system that produces publicly available real-time forecasts in more than 80 countries demonstrates the potential of ai to provide early and accurate flood warnings in ungauged basins. This marks significant progress in improving global access to reliable flood forecasts and early warning systems.
<h2 class="wp-block-heading" id="h-google-research-paper-ai-revolutionizes-flood-forecasting”>Google Research Paper: ai Revolutionizes Flood Forecasting
He google research paper presents a significant advance in flood forecasting using artificial intelligence (ai) trained on open, public data sets. The study evaluates the potential of ai to revolutionize global access to forecasts of extreme events on international rivers. Leveraging ai, an operating system has been developed to produce short-term (7-day) flood forecasts in more than 80 countries, providing real-time forecasts without barriers to access such as monetary charges or website registration.
<h3 class="wp-block-heading" id="h-using-ai-for-global-flood-forecasts”>Using ai for global flood forecasting
The Google research paper delves into the use of ai for global flood forecasting, highlighting the development of an ai-enabled streamflow prediction model that expands on previous work on hydrological nowcasting models. The model uses long short-term memory (LSTM) networks to predict daily flow over a 7-day forecast horizon. Notably, the ai model does not use flow data as inputs, which addresses the challenge of real-time data availability, especially in non-gauged locations. The model architecture incorporates an encoder-decoder model with separate LSTM units for historical and forecast meteorological input data.
From open data to real-time forecasts
The operating system developed based on the ai model provides real-time flood forecasts in more than 80 countries, marking an important milestone in improving global access to reliable flood warnings. The ability of the system to produce short-term forecasts without barriers to access, as evidenced by the availability of real-time forecasts.
and free of charge, highlights the potential of ai to improve flood early warning systems.
Beyond the latest in technology
The performance of the ai model surpasses the current state-of-the-art global modeling system, the Copernicus Emergency Management Service Global Flood Awareness System (GloFAS). The study reports that the ai-based forecast achieves reliability in predicting extreme river events in ungauged basins with a lead time of up to five days, comparable to or better than the reliability of GloFAS nowcasts. Additionally, the ai model's accuracy on five-year return period events is similar to or better than current accuracies on one-year return period events, indicating its potential to provide early and accurate flood warnings on events. larger and with greater impact in uncalibrated basins.
<h2 class="wp-block-heading" id="h-under-the-hood-the-ai-model”>Under the hood: the ai model
Building the brains
The ai streamflow prediction model extends previous work on hydrological nowcasting models by using LSTM networks to simulate streams of streamflow data from meteorological input data. The model uses an encoder-decoder architecture with one LSTM running on a historical sequence of input weather data (the LSTM encoder) and another LSTM running on the 7-day forecast horizon with weather forecast inputs (the decoder LSTM). The model does not use flow data as inputs due to the unavailability of real-time data at unmeasured locations, and the benchmark (GloFAS) does not use autoregressive inputs. The data set includes model inputs and flow targets for 152,259 years from 5,680 watersheds, with a total size of 60 GB saved on disk.
The data timeline
The figure shows the data periods available from each source used for training and prediction with the ai model. During training, missing data were imputed using a similar variable from another data source or by imputing with a mean value and adding a binary flag to indicate an imputed value. The model uses a 365-day long lookback sequence, with a hidden size of 256 cell states for the encoder and decoder LSTMs.
<h3 class="wp-block-heading" id="h-how-well-does-the-ai-model-predict”>How well does the ai model predict?
The performance of the ai model was evaluated using cross-validation experiments, using data from 5,680 meters partitioned in time and space to ensure out-of-sample predictions. The model predicts parameters from a unique asymmetric Laplacian distribution over the area-normalized streamflow at each time step and forecasts the delivery time. The model was trained on 50,000 mini-batches with a batch size of 256 and standardized inputs by subtracting the mean and dividing by the standard deviation of the training period data.
Putting the model to the test
Cross-validation experiments included divisions between continents, climatic zones, and groups of hydrologically separated basins. The ai model was evaluated out-of-sample in both location and time, and results were reported on a hydrograph that resulted from the average of the predicted hydrographs from a set of three separately trained encoder and decoder LSTMs.
Model evaluation with hydrograph metrics
Hydrograph metrics for the ai model and GloFAS overall assessment gauges were evaluated, with scores decreasing as lead time increased. Results were calculated for the period 2014-2021 and metrics were listed in Extended Data Table 1. Additionally, hydrograph metrics for the ai model and GloFAS were evaluated at the 1144 gauges where GloFAS is calibrated, with scores decreasing as lead increases. time.
<h3 class="wp-block-heading" id="h-what-makes-the-ai-tick”>What motivates ai?
Feature importance ratings from reliability classifiers were used to indicate which geophysical attributes determine high versus low reliability in the ai model. The most essential features of the ai model included drainage area, mean annual potential evapotranspiration (PET), mean annual actual evapotranspiration (AET), and elevation. These attributes were correlated with reliability scores, indicating a high degree of nonlinearity and parameter interaction in the model.
Conclusion
While hydrological models have matured, many flood-prone regions lack reliable forecasting and early warning systems. Google's research paper demonstrates how leveraging artificial intelligence and open data can significantly improve the accuracy, recall, and lead time of short-term forecasts for extreme river events. ai-based forecasts offer a promising solution by extending the reliability of current global forecasts to a five-day lead time and improving forecasting skills in Africa to levels comparable to those in Europe.
Additionally, providing these forecasts publicly in real time and without barriers to access allows for timely dissemination of flood warnings. Despite this progress, there is room for further improvement by increasing access to hydrological data to train accurate models and real-time updates through open source initiatives such as Caravan. Improving global flood predictions and early warnings is critical to protecting millions of people around the world from the devastating effects of flooding on lives and property. The combination of ai, open data and collaborative efforts paves the way towards this vital goal.
