Investigating the impact of the Chi‐Chi earthquake on the occurrence of debris flows using artificial neural networks

Abstract

Abstract:Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi-Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen-Yu-Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96Ð5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi-Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions.

A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

“…The variables that determine the debris-flow hazard of an area can be grouped into two general categories: 1) the intrinsic or static variables that contribute to debris-flow susceptibility, such as geology, slope gradient, slope aspect, longterm drainage patterns, elevation, and material cohesion; and 2) the dynamic or extrinsic variables that tend to trigger debris flows in an area of given susceptibility, such as heavy rainfall events and earthquakes [3], [5], [12], [50]. In this study, we are carrying on a debris-flow susceptibility investigation that is by definition the spatial analysis of the intrinsic factors; it expresses the likelihood of debris-flow occurrences at each spatial element [21].…”

Evolution of debris flow properties and physical interactions in debris-flow mixtures in the Wenchuan earthquake zone

“…Lin et al, 2003;Dadson et al, 2004;Koi et al, 2008;Liu et al, 2008;Chang et al, 2009;Chen et al, 2011;; J.C. Tang et al, 2012;Xu et al, 2012). Due to earthquake-induced landslides and other reasons, the activity of debris flows may change significantly after a strong earthquake compared with that before the earthquake (Dadson et al, 2004;Koi et al, 2008; J.C. .…”

“…The magnitude and frequency of debris flows can increase sharply while the critical rainfall to trigger the debris flows can decrease obviously (e.g. Lin et al, 2003;Chang et al, 2009;Chen et al, 2011;Tang et al, 2012). A stronger ground motion would lead to a larger change in debris flow density (Liu et al, 2008).…”