Deep Learning for Enhanced Multisensor Quantitative Precipitation Estimation

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Accurate and reliable quantitative precipitation estimations (QPE) are of particular importance for diverse hydrologic applications, sustainable water resources planning and management, forest fire risk rating, and drought risk management in agriculture. Specifically, high-resolution QPE are essential for hydrologic modelling and forecasting, optimal reservoir operation and scheduling, improved forecasts of landslides, extreme events (floods, droughts), including seasonal outlooks for domestic and industrial water supply. However, existing QPE are limited in accuracy, reliability, and in their spatial and temporal resolution. The proposed research seeks to address these limitations by resorting to recent advances in artificial intelligence, land surface monitoring technology (satellites, radars, weather stations), and in meteorology (improved numerical weather predictions).The main objective of this research program is to develop an innovative quantitative precipitation estimation system to enhance the accuracy and reliability of estimated precipitation. The specific research objectives include : (1) investigating emerging machine learning techniques (namely deep learning algorithms) for developing QPE models; (2) developing new methods for merging QPE from multiple models, (3) developing and assessing the novel multi-model QPE system across Canada. For the first research objective, adequate deep learning methods will be identified and used to develop effective QPE models. To achieve the second objective, a copula-Bayesian averaging approach will be extended based on entropy theory, to develop a more robust model fusion technique. Finally, the latter will be used with the deep learning based models to develop the multi-model QPE system.Given the importance of QPE in hydrology and the water sector at large, and the diverse societal needs of QPE, the proposed QPE system will be highly beneficial to a wide range of users across Canada and beyond. Furthermore, the research program will train a new generation of highly skilled personnel who will be well equipped to exploit recent advances in machine learning technology to take full advantage of the large datasets been made available by modern observing technology.