Spatiotemporal modelling of rainfall-induced landslides using machine learning

Ng, Charles Wang Wai; Bin, Yang; Liu, Zhongqiang; Kwan, J.S.H.; Lei, Chen

doi:10.1007/s10346-021-01662-0

Cited by 44 publications

(14 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such an imbalance can cause a model to be biased towards classifying the susceptible area as safe (i.e., negative value), jeopardizing the accuracy of the minority class prediction. Ma et al (2021) [15] outlined three typical solutions for overcoming this problem, out of which the data-level technique was commonly adopted in previous studies [10,11,16,17]. This method involves a selection of a 1:1 ratio (or other ratio as appropriate) of landsliding data points to non-landsliding data.…”

Section: The Modelling Approachmentioning

confidence: 99%

“…(2) Machine learning (ML)-based approach: Dai & Lee (2002) [10] adopted the logistic multiple regression method to categorize the relative landslide susceptibility of the natural terrain on an outlying island of Hong Kong. Recently, Ng et al (2021) [11] carried out a territory-wide spatiotemporal modelling of rainfall-induced natural terrain landslides with ML and deep learning algorithms considering storm-based data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study

Li¹,

Lo²,

Wong³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Practical landslide predictions are instrumental to effective landslide risk management. Recently, the use of machine learning (ML) has become a promising alternative means for landslide predictions. This paper discusses the recent progress of a pilot study of ML-powered rainfall-based natural terrain landslide susceptibility analysis in Hong Kong. This study is different to other similar studies in that: (1) data sampling commonly used to deal with an imbalanced dataset is not adopted, and (2) the incorporation of domain knowledge on landslide characteristics for the development of physically meaningful ML models. The results are found to be promising, with the achieved ROC AUC up to 91.5% based on the testing data. The resolution of the susceptibility map is enhanced by approximately three orders of magnitude further than the introduction of additional features critically selected with feature engineering and based on domain knowledge and past experiences.

show abstract

Section: The Modelling Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study

Li¹,

Lo²,

Wong³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Data-driven machine-learning models are increasingly employed in improving early warning models, weather and natural hazard forecasts, and disaster evacuation management. Examples include weather forecasting 20,21 , landslide displacement prediction 22 , spatial mapping of debris flow susceptibility [23][24][25][26] , predicting scales of landslides 27 and monthly rainfall for early warning of landslide occurrence 28 , differentiating between ground vibrations generated by debris flows and other seismic signals 29 , and enhancing disaster response and emergency evacuation planning [30][31][32][33] . However, to the best of our knowledge, none of the existing studies predict the occurrences of debris flows within a selected time using machine learning algorithms trained on historical hourly rainfall data alone.…”

Section: Introductionmentioning

confidence: 99%

Improving debris flow evacuation alerts in Taiwan using machine learning

Tsai

Irvin

Chundi

et al. 2022

Preprint

View full text Add to dashboard Cite

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts.

show abstract

“…The importance of every variable in predicting the FS has been also defined. In the literature, machine learning algorithms are often used to predict landslides at a regional scale (Liu et al 2020;Ng et al 2021) and seldom at a slope scale.…”

Section: Introductionmentioning

confidence: 99%

A first step towards a IoT-based local early warning system for an unsaturated slope in Norway

2022

View full text Add to dashboard Cite

A framework for a IoT-based local landslide early warning system (Lo-LEWS) has been proposed. Monitoring, modelling, forecasting and warning represent the main phases of the proposed framework. In this study, the first two phases have been applied to capture the hydrological behaviour of a natural unsaturated slope located adjacent to a railway track in Eastern Norway. The slope is monitored and the stability is kept under frequent observation, due to its steepness and the presence of the railway lines at the toe. The commercial software GeoStudio SEEP was used to create and calibrate a model able to replicate the in situ monitored volumetric water content (VWC) and pore water pressure (PWP) regime. The simulations conducted were divided into two main series: one with an initial calibration of the VWC profile (C) and another with no calibration (NC). The simulations have been validated using Taylor diagrams, which graphically summarize how closely a pattern (or a set of patterns) matches observations. The results show that a preliminary calibration for matching the in situ VWC, as well as considering climate conditions and vegetation, are crucial aspects to model the response of the studied unsaturated slope. A sensitivity analysis on the hydraulic conductivity and the permeability anisotropy ratio contributed to better define the input data and to improve the best-fit model result. The effectiveness of the best simulation, in back-calculating VWC, was tested for 3 different time periods: 6-month, 1-year, 1.25-year. The results show that the hydrological model can adequately represent the real monitored conditions up to a 1-year period, a recalibration is needed afterward. In addition, a slope stability analysis with GeoStudio SLOPE for the 1-year period was coupled to the hydrological model. Finally, the calculated safety factor (FS), the temperature, the precipitation, the VWC and PWP monitored were used as input dataset for a supervised machine learning algorithm. A random forest model highlighted the importance of the monitored VWC for forecasting the FS. The findings presented in this paper can be seen as a first step towards an Internet of Things (IoT)-based real-time slope stability analysis that can be employed as Lo-LEWS.

show abstract

Spatiotemporal modelling of rainfall-induced landslides using machine learning

Cited by 44 publications

References 47 publications

Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study

Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study

Improving debris flow evacuation alerts in Taiwan using machine learning

A first step towards a IoT-based local early warning system for an unsaturated slope in Norway

Contact Info

Product

Resources

About