Zvika Ben-Haim
Zvika Ben-Haim received the B.Sc. degree in electrical engineering and the B.A. degree in physics in 2000, the M.Sc. in electrical engineering in 2005, and the Ph.D. degree in electrical engineering in 2010, all from the Technion---Israel Institute of Technology, Haifa, Israel. He is currently with the Google Israel R&D Center.
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Flood forecasting with machine learning models in an operational framework
Asher Metzger
Chen Barshai
Dana Weitzner
Frederik Kratzert
Gregory Begelman
Guy Shalev
Hila Noga
Moriah Royz
Niv Giladi
Ronnie Maor
Sella Nevo
Yotam Gigi
HESS (2022)
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Google’s operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the Long Short-Term Memory (LSTM) networks and the Linear models. Flood inundation is computed with the Thresholding and the Manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The Manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the Linear model, while the Thresholding and Manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287,000 km2, home to more than 350M people. More than 100M flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations, as well as improving modeling capabilities and accuracy.
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Physics aware downsampling with deep learning for scalable flood modeling
Niv Giladi
Sella Nevo
Daniel Soudry
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (2021)
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Background: Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions. Flood forecasting is therefore a vitally important endeavor, typically achieved using physical water flow simulations, which rely on accurate terrain elevation maps. However, such simulations, based on solving partial differential equations, are computationally prohibitive on a large scale. This scalability issue is commonly alleviated using a coarse grid representation of the elevation map, though this representation may distort crucial terrain details, leading to significant inaccuracies in the simulation.
Contributions: We train a deep neural network to perform physics-informed downsampling of the terrain map: we optimize the coarse grid representation of the terrain maps, so that the flood prediction will match the fine grid solution. For the learning process to succeed, we configure a dataset specifically for this task. We demonstrate that with this method, it is possible to achieve a significant reduction in computational cost, while maintaining an accurate solution. A reference implementation accompanies the paper as well as documentation and code for dataset reproduction.
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Inundation Modeling in Data Scarce Regions
Vova Anisimov
Yusef Shafi
Sella Nevo
Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (2019)
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Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providing spatially accurate forecasts is a challenge due to scarcity of data and lack of funding. This paper describes an operational system providing flood extent forecast maps covering several flood-prone regions in India, with the goal of being sufficiently scalable and cost-efficient to facilitate the establishment of effective flood forecasting systems globally.
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Performance bounds and design criteria for estimating finite rate of innovation signals
Tomer Michaeli
Yonina C. Eldar
IEEE Transactions on Information Theory, vol. 58 (2012), pp. 4993-5015
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In this paper, we consider the problem of estimating finite rate of innovation (FRI) signals from noisy measurements, and specifically analyze the interaction between FRI techniques and the underlying sampling methods. We first obtain a fundamental limit on the estimation accuracy attainable regardless of the sampling method. Next, we provide a bound on the performance achievable using any specific sampling approach. Essential differences between the noisy and noise-free cases arise from this analysis. In particular, we identify settings in which noise-free recovery techniques deteriorate substantially under slight noise levels, thus quantifying the numerical instability inherent in such methods. This instability, which is only present in some families of FRI signals, is shown to be related to a specific type of structure, which can be characterized by viewing the signal model as a union of subspaces. Finally, we develop a methodology for choosing the optimal sampling kernels for linear reconstruction, based on a generalization of the Karhunen–Loeve transform. The results are illustrated for several types of time-delay estimation problems.
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Unbiased Estimation of a Sparse Vector in Gaussian Noise
Alexander Jung
Franz Hlawatsch
Yonina C. Eldar
IEEE Transactions on Information Theory, vol. 57(12) (2011), : 7856-7876
The Cramer--Rao Bound for Estimating a Sparse Parameter Vector
Yonina C. Eldar
IEEE Transactions on Signal Processing, vol. 58 (2010), pp. 3384-3389
Coherence-Based Near-Oracle Performance Guarantees for Sparse Estimation Under Gaussian Noise
Yonina C. Eldar
Michael Elad
Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP 2010), Dallas, TX, pp. 3590-3593
Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise
Yonina C. Eldar
Michael Elad
IEEE Transactions on Signal Processing, vol. 58 (2010), pp. 5030-5043
On Unbiased Estimation of Sparse Vectors Corrupted by Gaussian Noise
Alexander Jung
Franz Hlawatsch
Yonina C. Eldar
Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP 2010), Dallas, TX, pp. 3990-3993
Performance bounds for sparse estimation with random noise
Yonina C. Eldar
Proc. IEEE Workshop on Statistical Signal Processing, Cardiff, Wales, UK (2009)
On the Constrained Cramer-Rao Bound with a Singular Fisher Information Matrix
A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function
Yonina C. Eldar
IEEE Transactions on Information Theory, vol. 55 (2008), pp. 5179-5196
A Comment on the Use of the Weiss--Weinstein Bound with Constrained Parameter Sets
Yonina C. Eldar
IEEE Transactions on Information Theory, vol. 54 (2008), pp. 4682-4684
Recursive blind minimax estimation: Improving MSE over recursive least squares
Asaf Elron
Guy Leibovitz
Yonina C. Eldar
Proc. 25th IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI'08) (2008)
A Bayesian estimation bound based on the optimal bias function
Yonina C. Eldar
Proc. 2nd Int. Workshop on Computational Adv. in Multi-Sensor Adapt. Process. (CAMSAP 2007), St. Thomas, U.S. Virgin Islands
Blind Minimax Estimation
Yonina C. Eldar
IEEE Transactions on Information Theory, vol. 53 (2007), pp. 3145-3157
Minimax Estimators Dominating the Least-Squares Estimator
Yonina C. Eldar
Proc. Int. Conf. Acoust., Speech and Signal Processing (ICASSP 2005), Philadelphia, PA, pp. 53-56
Maximum Set Estimators with Bounded Estimation Error
Yonina C. Eldar
IEEE Transactions on Signal Processing, vol. 53 (2005), pp. 3172-3182
Blind Minimax Estimators: Improving on Least-Squares Estimation
Yonina C. Eldar
Proc. IEEE Workshop on Statistical Signal Processing (SSP 2005), Bordeaux, France, pp. 545-550
Estimation with maximum error requirements
Yonina C. Eldar
Proc. IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI 2004), Tel-Aviv, Israel, pp. 416-419
