Application of Machine Learning Techniques in Hydrometeorological Event Prediction
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Abstract:
Hydrometeorological events, such as floods and droughts, pose significant challenges to societies worldwide, causing loss of life and economic damage. Traditional methods of predicting such events often rely on statistical and physical models that are limited by their assumptions, uncertainties, and computational requirements. Machine learning (ML) techniques, with their ability to extract knowledge and insights from data, have shown great potential for improving the accuracy and lead time of hydrometeorological event prediction. This chapter reviews the use of ML for predicting hydrometeorological events focusing on flood and drought events. The chapter provides an overview of the application of ML techniques or algorithms for hydrometeorological events prediction. The chapter discusses data type, collection, and analysis for ML applications for predicting hydrometeorological events. The chapter presents case studies from different regions and highlights the benefits of ML-based approaches and the challenges. Finally, the chapter identifies future research directions.Keywords:
Hydrometeorology
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Summary November 19, 2021, marks the 100th anniversary of the National Hydrometeorological Service of Ukraine. This article presents results of studies devoted to the history of formation and development of multifaceted activities of the Hydrometeorological Service, its importance in ensuring the sustainable socio-economic development of the country. The main attention is focused on consideration of the period that began from 1991 - the year of the proclamation of the state independence of Ukraine and the beginning of formation of the National Hydrometeorological Service. In 1999, the Law of Ukraine “On Hydrometeorological Activity” was adopted, in which the term “National Hydrometeorological Service” appeared firstly. A number of legal acts in the field of hydrometeorological activities were adopted by the Ukrainian Government, modern methods and technologies for conducting meteorological and hydrological observations and forecasting were introduced. In the difficult economic conditions, the Hydrometeorological Service managed to ensure the development in many areas of activities, to maintain, and in some areas, to expand observation networks, to introduce new types of hydrometeorological products and services for end - users. All these measures allowed the Hydrometeorological Service to gain the respect of others hydrometeorological services of Europe and the World Meteorological Organization.
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Reconstructions of hydrometeorological changes and human impacts using subsurface environmental data
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Hydrometeorology aims at measuring and understanding the physics, chemistry, energy and water fluxes of the atmosphere, and their coupling with the earth surface environmental parameters. Accurate hydrometeorological records and observations with different timelines are crucial to assess climate evolution and weather forecast. Historical records suggest that the first hydrometeorological observations date back to ca 3500 BC. Reviewing these observations in the light of our modern knowledge of the dynamic of atmospheres is critical as it can reduce the ambiguities associated to understanding major fluctuations or evolutions in the earth climate. Today, the ambiguities in hydrometeorological observations have significantly improved due to the advances in monitoring, modeling, and forecasting of processes related to the land-atmosphere coupling and forcing. Numerical models have been developed to forecast hydrometeorological phenomena in short-, medium- and long-term horizons, ranging from hourly to annual timescales. We provide herein a synthetic review of advances in hydrometeorological observations from their infancy to today. In particular, we discuss the role of hydrometeorological records, observations, and modeling in assessing the amplitude and time-scale for climate change and global warming.
Hydrometeorology
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Hydrometeorological patterns can be defined as meaningful and nontrivial associations between hydrological and meteorological parameters over a region. Discovering hydrometeorological patterns is important for many applications, including forecasting hydrometeorological hazards (floods and droughts), predicting the hydrological responses of ungauged basins, and filling in missing hydrological or meteorological records. However, discovering these patterns is challenging due to the special characteristics of hydrological and meteorological data, and is computationally complex due to the archival history of the datasets. Moreover, defining monotonic interest measures to quantify these patterns is difficult. In this study, we propose a new monotonic interest measure, called the hydrometeorological prevalence index, and a novel algorithm for mining hydrometeorological patterns (HMP-Miner) out of large hydrological and meteorological datasets. Experimental evaluations using real datasets show that our proposed algorithm outperforms the naïve alternative in discovering hydrometeorological patterns efficiently.
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