Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research

Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to climate change. Nearly 1.5 billion people, making up 19% of the world’s population, are exposed to substantial risks from severe flood events. Upgrading early warning systems to make accurate and timely information accessible to these populations can save thousands of lives per year.

Driven by the potential impact of reliable flood forecasting on people’s lives globally, we started our flood forecasting effort in 2017. Through this multi-year journey, we advanced research over the years hand-in-hand with building a real-time operational flood forecasting system that provides alerts on Google Search, Maps, Android notifications and through the Flood Hub. However, in order to scale globally, especially in places where accurate local data is not available, more research advances were required.

In “Global prediction of extreme floods in ungauged watersheds”, published in Nature, we demonstrate how machine learning (ML) technologies can significantly improve global-scale flood forecasting relative to the current state-of-the-art for countries where flood-related data is scarce. With these AI-based technologies we extended the reliability of currently-available global nowcasts, on average, from zero to five days, and improved forecasts across regions in Africa and Asia to be similar to what are currently available in Europe. The evaluation of the models was conducted in collaboration with the European Center for Medium Range Weather Forecasting (ECMWF).

These technologies also enable Flood Hub to provide real-time river forecasts up to seven days in advance, covering river reaches across over 80 countries. This information can be used by people, communities, governments and international organizations to take anticipatory action to help protect vulnerable populations.

Flood forecasting at Google

The ML models that power the FloodHub tool are the product of many years of research, conducted in collaboration with several partners, including academics, governments, international organizations, and NGOs.

In 2018, we launched a pilot early warning system in the Ganges-Brahmaputra river basin in India, with the hypothesis that ML could help address the challenging problem of reliable flood forecasting at scale. The pilot was further expanded the following year via the combination of an inundation model, real-time water level measurements, the creation of an elevation map and hydrologic modeling.

In collaboration with academics, and, in particular, with the JKU Institute for Machine Learning we explored ML-based hydrologic models, showing that LSTM-based models could produce more accurate simulations than traditional conceptual and physics-based hydrology models. This research led to flood forecasting improvements that enabled the expansion of our forecasting coverage to include all of India and Bangladesh. We also worked with researchers at Yale University to test technological interventions that increase the reach and impact of flood warnings.

Our hydrological models predict river floods by processing publicly available weather data like precipitation and physical watershed information. Such models must be calibrated to long data records from streamflow gauging stations in individual rivers. A low percentage of global river watersheds (basins) have streamflow gauges, which are expensive but necessary to supply relevant data, and it’s challenging for hydrological simulation and forecasting to provide predictions in basins that lack this infrastructure. Lower gross domestic product (GDP) is correlated with increased vulnerability to flood risks, and there is an inverse correlation between national GDP and the amount of publicly available data in a country. ML helps to address this problem by allowing a single model to be trained on all available river data and to be applied to ungauged basins where no data are available. In this way, models can be trained globally, and can make predictions for any river location.

There is an inverse (log-log) correlation between the amount of publicly available streamflow data in a country and national GDP. Streamflow data from the Global Runoff Data Center.

Our academic collaborations led to ML research that developed methods to estimate uncertainty in river forecasts and showed how ML river forecast models synthesize information from multiple data sources. They demonstrated that these models can simulate extreme events reliably, even when those events are not part of the training data. In an effort to contribute to open science, in 2023 we open-sourced a community-driven dataset for large-sample hydrology in Nature Scientific Data.

The river forecast model

Most hydrology models used by national and international agencies for flood forecasting and river modeling are state-space models, which depend only on daily inputs (e.g., precipitation, temperature, etc.) and the current state of the system (e.g., soil moisture, snowpack, etc.). LSTMs are a variant of state-space models and work by defining a neural network that represents a single time step, where input data (such as current weather conditions) are processed to produce updated state information and output values (streamflow) for that time step. LSTMs are applied sequentially to make time-series predictions, and in this sense, behave similarly to how scientists typically conceptualize hydrologic systems. Empirically, we have found that LSTMs perform well on the task of river forecasting.

A diagram of the LSTM, which is a neural network that operates sequentially in time. An accessible primer can be found here.

Our river forecast model uses two LSTMs applied sequentially: (1) a “hindcast” LSTM ingests historical weather data (dynamic hindcast features) up to the present time (or rather, the issue time of a forecast), and (2) a “forecast” LSTM ingests states from the hindcast LSTM along with forecasted weather data (dynamic forecast features) to make future predictions. One year of historical weather data are input into the hindcast LSTM, and seven days of forecasted weather data are input into the forecast LSTM. Static features include geographical and geophysical characteristics of watersheds that are input into both the hindcast and forecast LSTMs and allow the model to learn different hydrological behaviors and responses in various types of watersheds.

Output from the forecast LSTM is fed into a “head” layer that uses mixture density networks to produce a probabilistic forecast (i.e., predicted parameters of a probability distribution over streamflow). Specifically, the model predicts the parameters of a mixture of heavy-tailed probability density functions, called asymmetric Laplacian distributions, at each forecast time step. The result is a mixture density function, called a Countable Mixture of Asymmetric Laplacians (CMAL) distribution, which represents a probabilistic prediction of the volumetric flow rate in a particular river at a particular time.

LSTM-based river forecast model architecture. Two LSTMs are applied in sequence, one ingesting historical weather data and one ingesting forecasted weather data. The model outputs are the parameters of a probability distribution over streamflow at each forecasted timestep.

Input and training data

The model uses three types of publicly available data inputs, mostly from governmental sources:

1. Static watershed attributes representing geographical and geophysical variables: From the HydroATLAS project, including data like long-term climate indexes (precipitation, temperature, snow fractions), land cover, and anthropogenic attributes (e.g., a nighttime lights index as a proxy for human development).
2. Historical meteorological time-series data: Used to spin up the model for one year prior to the issue time of a forecast. The data comes from NASA IMERG, NOAA CPC Global Unified Gauge-Based Analysis of Daily Precipitation, and the ECMWF ERA5-land reanalysis. Variables include daily total precipitation, air temperature, solar and thermal radiation, snowfall, and surface pressure.
3. Forecasted meteorological time series over a seven-day forecast horizon: Used as input for the forecast LSTM. These data are the same meteorological variables listed above, and come from the ECMWF HRES atmospheric model.

Training data are daily streamflow values from the Global Runoff Data Center over the time period 1980 – 2023. A single streamflow forecast model is trained using data from 5,680 diverse watershed streamflow gauges (shown below) to improve accuracy.

Location of 5,680 streamflow gauges that supply training data for the river forecast model from the Global Runoff Data Center.

Improving on the current state-of-the-art

We compared our river forecast model with GloFAS version 4, the current state-of-the-art global flood forecasting system. These experiments showed that ML can provide accurate warnings earlier and over larger and more impactful events.

The figure below shows the distribution of F1 scores when predicting different severity events at river locations around the world, with plus or minus 1 day accuracy. F1 scores are an average of precision and recall and event severity is measured by return period. For example, a 2-year return period event is a volume of streamflow that is expected to be exceeded on average once every two years. Our model achieves reliability scores at up to 4-day or 5-day lead times that are similar to or better, on average, than the reliability of GloFAS nowcasts (0-day lead time).

Distributions of F1 scores over 2-year return period events in 2,092 watersheds globally during the time period 2014-2023 from GloFAS (blue) and our model (orange) at different lead times. On average, our model is statistically as accurate as GloFAS nowcasts (0–day lead time) up to 5 days in advance over 2-year (shown) and 1-year, 5-year, and 10-year events (not shown).

Additionally (not shown), our model achieves accuracies over larger and rarer extreme events, with precision and recall scores over 5-year return period events that are similar to or better than GloFAS accuracies over 1-year return period events. See the paper for more information.

Looking into the future

The flood forecasting initiative is part of our Adaptation and Resilience efforts and reflects Google’s commitment to address climate change while helping global communities become more resilient. We believe that AI and ML will continue to play a critical role in helping advance science and research towards climate action.

We actively collaborate with several international aid organizations (e.g., the Centre for Humanitarian Data and the Red Cross) to provide actionable flood forecasts. Additionally, in an ongoing collaboration with the World Meteorological Organization (WMO) to support early warning systems for climate hazards, we are conducting a study to help understand how AI can help address real-world challenges faced by national flood forecasting agencies.

While the work presented here demonstrates a significant step forward in flood forecasting, future work is needed to further expand flood forecasting coverage to more locations globally and other types of flood-related events and disasters, including flash floods and urban floods. We are looking forward to continuing collaborations with our partners in the academic and expert communities, local governments and the industry to reach these goals.

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Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research

Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language (e.g., icons and layouts), that offer an opportunity to build a single model that can understand, reason, and interact with these interfaces. However, because of their complexity and varied presentation formats, infographics and UIs present a unique modeling challenge.

To that end, we introduce “ScreenAI: A Vision-Language Model for UI and Infographics Understanding”. ScreenAI improves upon the PaLI architecture with the flexible patching strategy from pix2struct. We train ScreenAI on a unique mixture of datasets and tasks, including a novel Screen Annotation task that requires the model to identify UI element information (i.e., type, location and description) on a screen. These text annotations provide large language models (LLMs) with screen descriptions, enabling them to automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. At only 5B parameters, ScreenAI achieves state-of-the-art results on UI- and infographic-based tasks (WebSRC and MoTIF), and best-in-class performance on Chart QA, DocVQA, and InfographicVQA compared to models of similar size. We are also releasing three new datasets: Screen Annotation to evaluate the layout understanding capability of the model, as well as ScreenQA Short and Complex ScreenQA for a more comprehensive evaluation of its QA capability.

ScreenAI

ScreenAI’s architecture is based on PaLI, composed of a multimodal encoder block and an autoregressive decoder. The PaLI encoder uses a vision transformer (ViT) that creates image embeddings and a multimodal encoder that takes the concatenation of the image and text embeddings as input. This flexible architecture allows ScreenAI to solve vision tasks that can be recast as text+image-to-text problems.

On top of the PaLI architecture, we employ a flexible patching strategy introduced in pix2struct. Instead of using a fixed-grid pattern, the grid dimensions are selected such that they preserve the native aspect ratio of the input image. This enables ScreenAI to work well across images of various aspect ratios.

The ScreenAI model is trained in two stages: a pre-training stage followed by a fine-tuning stage. First, self-supervised learning is applied to automatically generate data labels, which are then used to train ViT and the language model. ViT is frozen during the fine-tuning stage, where most data used is manually labeled by human raters.

ScreenAI model architecture.

Data generation

To create a pre-training dataset for ScreenAI, we first compile an extensive collection of screenshots from various devices, including desktops, mobile, and tablets. This is achieved by using publicly accessible web pages and following the programmatic exploration approach used for the RICO dataset for mobile apps. We then apply a layout annotator, based on the DETR model, that identifies and labels a wide range of UI elements (e.g., image, pictogram, button, text) and their spatial relationships. Pictograms undergo further analysis using an icon classifier capable of distinguishing 77 different icon types. This detailed classification is essential for interpreting the subtle information conveyed through icons. For icons that are not covered by the classifier, and for infographics and images, we use the PaLI image captioning model to generate descriptive captions that provide contextual information. We also apply an optical character recognition (OCR) engine to extract and annotate textual content on screen. We combine the OCR text with the previous annotations to create a detailed description of each screen.

A mobile app screenshot with generated annotations that include UI elements and their descriptions, e.g., TEXT elements also contain the text content from OCR, IMAGE elements contain image captions, LIST_ITEMs contain all their child elements.

LLM-based data generation

We enhance the pre-training data’s diversity using PaLM 2 to generate input-output pairs in a two-step process. First, screen annotations are generated using the technique outlined above, then we craft a prompt around this schema for the LLM to create synthetic data. This process requires prompt engineering and iterative refinement to find an effective prompt. We assess the generated data’s quality through human validation against a quality threshold.

You only speak JSON. Do not write text that isn’t JSON.
You are given the following mobile screenshot, described in words. Can you generate 5 questions regarding the content of the screenshot as well as the corresponding short answers to them? 

The answer should be as short as possible, containing only the necessary information. Your answer should be structured as follows:
questions: [
{{question: the question,
    answer: the answer
}},
 ...
]

{THE SCREEN SCHEMA}

By combining the natural language capabilities of LLMs with a structured schema, we simulate a wide range of user interactions and scenarios to generate synthetic, realistic tasks. In particular, we generate three categories of tasks:

• Question answering: The model is asked to answer questions regarding the content of the screenshots, e.g., “When does the restaurant open?”
• Screen navigation: The model is asked to convert a natural language utterance into an executable action on a screen, e.g., “Click the search button.”
• Screen summarization: The model is asked to summarize the screen content in one or two sentences.

Block diagram of our workflow for generating data for QA, summarization and navigation tasks using existing ScreenAI models and LLMs. Each task uses a custom prompt to emphasize desired aspects, like questions related to counting, involving reasoning, etc.

LLM-generated data. Examples for screen QA, navigation and summarization. For navigation, the action bounding box is displayed in red on the screenshot.

Experiments and results

As previously mentioned, ScreenAI is trained in two stages: pre-training and fine-tuning. Pre-training data labels are obtained using self-supervised learning and fine-tuning data labels comes from human raters.

We fine-tune ScreenAI using public QA, summarization, and navigation datasets and a variety of tasks related to UIs. For QA, we use well established benchmarks in the multimodal and document understanding field, such as ChartQA, DocVQA, Multi page DocVQA, InfographicVQA, OCR VQA, Web SRC and ScreenQA. For navigation, datasets used include Referring Expressions, MoTIF, Mug, and Android in the Wild. Finally, we use Screen2Words for screen summarization and Widget Captioning for describing specific UI elements. Along with the fine-tuning datasets, we evaluate the fine-tuned ScreenAI model using three novel benchmarks:

1. Screen Annotation: Enables the evaluation model layout annotations and spatial understanding capabilities.
2. ScreenQA Short: A variation of ScreenQA, where its ground truth answers have been shortened to contain only the relevant information that better aligns with other QA tasks.
3. Complex ScreenQA: Complements ScreenQA Short with more difficult questions (counting, arithmetic, comparison, and non-answerable questions) and contains screens with various aspect ratios.

The fine-tuned ScreenAI model achieves state-of-the-art results on various UI and infographic-based tasks (WebSRC and MoTIF) and best-in-class performance on Chart QA, DocVQA, and InfographicVQA compared to models of similar size. ScreenAI achieves competitive performance on Screen2Words and OCR-VQA. Additionally, we report results on the new benchmark datasets introduced to serve as a baseline for further research.

Comparing model performance of ScreenAI with state-of-the-art (SOTA) models of similar size.

Next, we examine ScreenAI’s scaling capabilities and observe that across all tasks, increasing the model size improves performances and the improvements have not saturated at the largest size.

Model performance increases with size, and the performance has not saturated even at the largest size of 5B params.

Conclusion

We introduce the ScreenAI model along with a unified representation that enables us to develop self-supervised learning tasks leveraging data from all these domains. We also illustrate the impact of data generation using LLMs and investigate improving model performance on specific aspects with modifying the training mixture. We apply all of these techniques to build multi-task trained models that perform competitively with state-of-the-art approaches on a number of public benchmarks. However, we also note that our approach still lags behind large models and further research is needed to bridge this gap.

Acknowledgements

This project is the result of joint work with Maria Wang, Fedir Zubach, Hassan Mansoor, Vincent Etter, Victor Carbune, Jason Lin, Jindong Chen and Abhanshu Sharma. We thank Fangyu Liu, Xi Chen, Efi Kokiopoulou, Jesse Berent, Gabriel Barcik, Lukas Zilka, Oriana Riva, Gang Li,Yang Li, Radu Soricut, and Tania Bedrax-Weiss for their insightful feedback and discussions, along with Rahul Aralikatte, Hao Cheng and Daniel Kim for their support in data preparation. We also thank Jay Yagnik, Blaise Aguera y Arcas, Ewa Dominowska, David Petrou, and Matt Sharifi for their leadership, vision and support. We are very grateful toTom Small for helping us create the animation in this post.

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