Estimation of Threshold Rainfall in Ungauged Areas Using Machine Learning

Abstract

In recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area (A), mean drainage elevation (H), mean drainage slope (S), drainage density (D), runoff curve number (CN), watershed parameter (Lp), and form factor (Rs) etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport (MOLIT) was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method (SVM), the random forest method, and eXtreme Gradient Boosting (XGBoost). As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE 0.28, and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding.

A review of flash‐floods management: From hydrological modeling to crisis management

“…Out of the 72 articles corresponding to query 4, more than half of the articles (39) were excluded from the criteria analysis (Section 3.2) (Alipour et al, 2020;Azam et al, 2017;Chen et al, 2019;Chu et al, 2022;Ciavola et al, 2018;Davies, 2015;Ding & Fang, 2019;Dresback et al, 2019;Habibi et al, 2021;Han & Sharif, 2021;Hofmann & Schüttrumpf, 2019;Ibarreche et al, 2020;Jha & Afreen, 2020;Jubach & Tokar, 2016;Kellens et al, 2013;Khan et al, 2018;Kim & Han, 2020;Kruczkiewicz et al, 2021;Li et al, 2022;Liu, Shi, & Fang, 2022;Liu, Zhou, et al, 2022;Lo et al, 2015;Munawar, Hammad, & Waller, 2021;Munawar, Ullah, et al, 2021;Nakamura & Morioka, 2019;Ngo et al, 2018;Nguyen et al, 2021;Perera et al, 2020;Pielke et al, 2021;Prikryl et al, 2021;Rasquinho et al, 2013;Saravi et al, 2019;Smith & Rodriguez, 2017;Stamellou et al, 2021;Tammar et al, 2020;Tariq et al, 2022;Wang, Chen, et al, 2021;Wang, Kong, et al, 2021;…”

Artificial Intelligence Techniques in Hydrology and Water Resources Management

“…Despite quantitative rainfall estimates from weather forecasts, it remains difficult to estimate the damage caused by rainfall. To address this issue, Chu et al employed various methods, such as support vector machine (SVM), random forest, and eXtreme Gradient Boosting (XGBoost), finding that XGBoost has the best performance [27]. Using XGBoost, the threshold rainfall of ungauged watersheds was calculated and verified using past rainfall events and damage cases, enabling the accurate prediction of flooding-induced rainfall and preparation for vulnerable areas.…”

Unraveling the complexities of urban fluvial flood hydraulics through AI

“…ML is rapidly growing in popularity across many fields. ML methods, including the emerging deep learning (DL) methods, have been successfully applied to the field of water resources for stage-discharge (Q/h) relationships 24 , rainfall-runoff 25 , sediment transport 26 , 27 , flood prediction 28 , water quality analysis 29 . AI models are specifically convenient when the uncertainty in model parameters, complexities in the physics-based equations and computational efforts are significantly high 30 , such as in urban hydrology.…”