Forecasting of Debris Flow Using Machine Learning-Based Adjusted Rainfall Information and RAMMS Model

Abstract

In recent years, climate change and extreme weather conditions have caused natural disasters of various sizes and forms across the world. The increase in the resulting flood damage and secondary damage has also inflicted massive social and economic harm. Korea is no exception, where debris flows created by typhoons and localized heavy rainfalls have caused human injuries and property damage in the Wumyeonsan Mountain in Seoul, Majeoksan Mountain in Chuncheon, Sinnam in Samcheok, Gokseong in Jeollanam-do, and Anseong in Gyeonggi-do. Disaster damage needs to be minimized by preparing for typhoons and heavy rainfalls that cause debris flow. To that end, we need accurate prediction of rainfall and flooding through simulations based on debris flow models. Most of the previous literature analyzed debris flows using rainfall events in the past before debris flow occurrence, rather than analyzing and predicting based on rainfall predictions. The main body of this study assesses the applicability of hydrological quantitative precipitation forecast (HQPF) generated through a machine learning method named the Random Forest (RF) method to debris flow analysis models. To that end, this study uses scatter plots to compare and analyze the precipitation observation data collected from the areas hit by debris flows in the past, and the quantitative precipitation forecast (QPF) and HQPF data from the Korea Meteorological Administration (KMA). Based on the verified HQPF data, runoff was calculated using the spatial runoff assessment tool (S-RAT) model, and the soil amount was calculated to simulate the debris flow damage with a two-dimensional rapid mass movements (RAMMS) model. The debris flow simulation based on the said data indicated varying degrees of flow depth, impact force, speed, and damage area depending on the precipitation. The correction of the HQPF was verified by measuring and comparing the spatial location accuracy by analyzing the Lee Sallee shape index (LSSI) of the damage areas. The findings confirm the correction of the HQPF based on machine learning and indicate its applicability to debris flow models.

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

“…Observation of flow-like landslide runouts was accomplished using the physical method, which was applied as a flume test, in studies by Baselt et al [12] and Gao et al [13]. The numerical model, which has frequently been used to scrutinize flow-like landslide runouts in recent studies, was also used in studies by Gao et al [13], Yang et al [14], Chae et al [15], Calista et al [16], Abraham et al [17], Dash et al [18], Vicari et al [19], Mikoš and Bezak [20], Oh et al [21], Zhou et al [22], Alene et al [23], and La Porta et al [24]. Investigations into the probabilities of failures were based on merged models in some studies.…”

Predicting Rainfall Using Random Forest and CatBoost Models

“…In recent times, the realm of machine learning (ML) has witnessed considerable progress. Earlier studies have utilized diverse machine learning techniques to predict rainfall events, including random forest (RF) [3], [4], XGBoost [5], deep learning [6] and artificial neural network (ANN) [7], [8], among others.…”