Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States

Kern, Ashley N.; Addison, Priscilla; Oommen, Thomas; Salazar, Sean E.; Coffman, Richard A.

doi:10.1007/s11004-017-9681-2

Cited by 63 publications

(52 citation statements)

References 33 publications

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“…(3) TRI TRI is increasingly being used in a topographical analysis to characterize the convex or concave forms of slopes [60,61]. It was developed by Riley [62] and can be computed with (Equation (2)):…”

Section: Factors Influencing Snow Avalanchesmentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

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Section: Factors Influencing Snow Avalanchesmentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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“…Other researchers have also looked into nonlinear probability modeling approach to investigate if more of the complex relationships between basin predictors and debris flow occurrence, which might not be discernible to linear models, can be captured with the nonlinear approach. Kern et al, 2017 explored the use of machine learning algorithms to model debris flow response. Their study explored both linear and nonlinear relationships between the predictors and response variable.…”

Section: Introductionmentioning

confidence: 99%

“…Their results showed the nonlinear models outperformed the linear ones by as much as $64% giving credence to their hypothesis that the relationship between basin predictors and the debris flows occurrence might be a nonlinear one. The top model identified from the Kern et al (2017) study was one that was built using the Naïve Bayes algorithm. This model resulted in a sensitivity of 72%, an improvement on the 44% that was initially obtained from Cannon et al, 2010's study, and a corresponding specificity of 90% showing improved ability to predict these debris flow locations with the nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

Assessment of post-wildfire debris flow occurrence using classifier tree

Addison

Oommen

Sha

2019

Geomatics, Natural Hazards and Risk

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Besides the dangers of an actively burning wildfire, a plethora of other hazardous consequences can occur afterwards. Debris flows are among the most hazardous of these, being known to cause fatalities and extensive damage to infrastructure. Although debris flows are not exclusive to fire affected areas, a wildfire can increase a location's susceptibility by stripping its protective covers like vegetation and introducing destabilizing factors such as ash filling soil pores to increase runoff potential. Due to the associated dangers, researchers are developing statistical models to isolate susceptible locations. Existing models predominantly employ the logistic regression algorithm; however, previous studies have shown that the relationship between the predictors and response is likely better predicted using nonlinear modeling. We therefore propose the use of nonlinear C5.0 decision tree algorithm, a simple yet robust algorithm that uses inductive inference for categorical data modelling. It employs a tree-like decision making system that makes conditional statements to split data into homogeneous classes. Our results showed the C5.0 approach to produce stable and higher validation metrics in comparison to the logistic regression. A sensitivity of 81% and specificity of 78% depicts improved predictive capability and gives credence to the hypothesis that data relationships are likely nonlinear. ARTICLE HISTORY

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“…Concretely, data mining tools and techniques commonly convert the raw sensor data into a compressed representation with significant information well retained, such as summary statistics derived from time-series data [15], and Fourier and Wavelet spectrograms extraction for image/audio data [16]. At debris flow detection stage, various statistical machine learning techniques have been adopted to exploit the particular debris flow-induced patterns with the extracted feature representations, including support vector machine (SVM) [17], decision trees [18], and artificial neural networks (ANN) [19]. According to empirical studies, advanced machine learning methods are deemed to be well suited to handle the multivariate sensor data, which usually exhibits wide variability and complex non-linear distributions [20].…”

Section: Introductionmentioning

confidence: 99%

An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network

Kurashima

Kobayashi

et al. 2019

Remote Sensing

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Debris flow disasters pose a serious threat to public safety in many areas all over the world, and it may cause severe consequences, including losses, injuries, and fatalities. With the emergence of deep learning and increased computation powers, nowadays, machine learning methods are being broadly acknowledged as a feasible solution to tackle the massive data generated from geo-informatics and sensing platforms to distill adequate information in the context of disaster monitoring. Aiming at detection of debris flow occurrences in a mountainous area of Sakurajima, Japan, this study demonstrates an efficient in-situ monitoring system which employs state-of-the-art machine learning techniques to exploit continuous monitoring data collected by a wireless accelerometer sensor network. Concretely, a two-stage data analysis process had been adopted, which consists of anomaly detection and debris flow event identification. The system had been validated with real data and generated favorable detection precision. Compared to other debris flow monitoring system, the proposed solution renders a batch of substantive merits, such as low-cost, high accuracy, and fewer maintenance efforts. Moreover, the presented data investigation scheme can be readily extended to deal with multi-modal data for more accurate debris monitoring, and we expect to expend addition sensory measurements shortly.

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Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States

Cited by 63 publications

References 33 publications

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Assessment of post-wildfire debris flow occurrence using classifier tree

An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network

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