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Seoul National University (2020)

Prediction Model of Soil Drought Distribution Using Machine Learning Algorithms and Geospatial Data


Titre : Prediction Model of Soil Drought Distribution Using Machine Learning Algorithms and Geospatial Data

머신러닝/공간자료를 이용한 가뭄예측모델 개발 및 시뮬레이션 기반의 물관리정책 효과성 추정

Auteur : 박해경

Université de soutenance : Seoul National University

Grade : Doctor of Philosophy (PhD) 2020

Résumé partiel
This study presents the entire process of establishing a water management policy based on scientific methods through drought prediction. Accordingly, this thesis includes the development of the severe drought area prediction (SDAP) model, verification of the algorithms used in the proposed model, and an application of the proposed model to policymaking. The core technology of the SDAP model is the convergence of machine learning and geospatial science, which makes it possible to use geospatial data instead of tabular data to visualize prediction results. The SDAP model was developed by considering the importance and difficulty of forecasting short-term droughts and the allocation of priorities for rapid water supply in terms of the related policy. The background of the models development began from the fact that, despite the advancements of science and technology, it has become more difficult to predict the probability of precipitation and prepare for droughts based on this probability due to the increasingly abnormal climate that has been associated with global warming. In fact, during 2015–17, Korea suffered from unpredictable, severe spring droughts. Such droughts are predicted to increase due to the fluctuation of precipitation as warming increases according to Global Warming 1.5 °C, a special report by the Intergovernmental Panel on Climate Change (IPCC). Thus, water management policy has become increasingly important over recent decades. In particular, rural areas are directly adversely affected during drought periods ; hence, if soil droughts are not quickly resolved, crop damage could affect economic inflation and human life. The US National Drought Mitigation Center has recommended that water supply priorities be set before the occurrence of droughts and that they be implemented immediately when a drought commences in order to minimize damage. However, accurate drought predictions that are based on probabilistic precipitation are difficult because short-term droughts (i.e., lasting from several weeks to months) are at the boundary between weather and climate. The SDAP model can estimate the spatial distribution of a soil drought in advance by assuming the subsequent lack of rainfall over the short-term as opposed to the yes/no prediction of a drought. The characteristics of the SDAP model enable it to predict future droughts by training the actual past droughts by machine learning using satellite imagery and topographic data without precipitation data. Prediction results by the SDAP model therefore assist in the selection of water supply priority areas through the provision of visualized maps of the relative severity of a soil drought. In addition, overlaying these maps with water resources (reservoirs and groundwater) or land use maps can also help to rearrange priorities in consideration of local conditions. The study area in this research is the Gyeonggi Province, a southern metropolitan area in Korea, which has experienced droughts that are understood to be related to climate change. Python was used as the programming language to develop the SDAP model. Each chapter of this thesis consists of stand-alone papers with subtitles as follows. Chapter 1 : Prediction of Severe Drought Area Based on Random Forest : Using Satellite Image and Topography Data. This chapter covers the details of the SDAP model design, the consideration of training areas, and the models coverage, advantages, and limitations. The distribution of a soil drought is expressed as the soil moisture index (SMI) with a float number type between 0 and 1. The model development began with the idea that machine learning might allow training of the mechanisms between soil moisture and surface environments (e.g., vegetation, topography, water, and temperature) during a drought. Fifteen input variables corresponding to the surface environment were generated using Landsat-8 imagery and a digital elevation model (DEM).


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