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Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan

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Abstract

Landslide susceptibility study is a critically important topic throughout the globe owing to the social and financial catastrophes of landslides. The present research aims to evaluate as well as compare the proficiencies of six advanced machine learning techniques (MLTs) for mapping the landslide susceptibility of northern parts of Pakistan. The considered MLTs include Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Artificial Neural Network (ANN), Naive Bayes, Multivariate Adaptive Regression Spline (MARS), along with Random Forest. The present research was performed applying GIS and R programming language (an open-source software). Primarily, the landslide inventory map was formulated with the help of an overall 3251 historical landslide events obtained through a variety of data sources. All the historical landslide locations were arbitrarily split into two groups with a proportion of 70% for training plus 30% for validating purposes. In total, sixteen landslide influencing factors were considered for modeling landslide susceptibility. These factors comprise aspect, elevation, slope, lithology, fault density, land cover classification system, topographic wetness index, earthquake, sediment transport index, normalized difference vegetation index, rainfall, soil, stream power index, road density, profile curvature, and plan curvature. The receiver operating characteristic, the area under curve (AUC), and root mean square error approaches were employed to appraise, validate, and relate the performance of the practiced MLTs. The outcomes demonstrated that AUC for six MLTs vary from 88.5% for LDA to 92.3% for ANN. The results reveal that among the six practiced MLTs, ANN (AUC = 92.3%) and MARS (AUC = 91.7%) have shown outstanding performances. Policymakers can use the findings of the present research and the produced landslide susceptibility maps for devising mitigation measures to curb the damages.

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Data availability

The data used for this study can be obtained from the corresponding author on a reasonable request.

Notes

  1. A flume test was conducted to evaluate the failure mechanism of a rainfall-induced landslide and to develop a physically based warning system. The test was performed at full scale to prevent scale effects, and the flume was a rectangular channel that was 20 m long, 4 m wide, and 2.5 m deep. The volumetric water content and the matric suction were measured at various depths to determine the rainfall infiltration into partially saturated soil. The displacement and tilt were measured at the slope surface, and a video camera was installed to record the slope failure. The results showed that the rainfall infiltration caused the volumetric water content to gradually increase and the matric suction to decrease. The resulting decrease in the soil strength caused soil deformation. Thus, the rainfall induced a landslide. The matric suction and the degree of saturation were used to calculate the generalized effective stress of the solid skeleton to develop a warning system. The stress paths were calculated using the effective mean stress and the deviatoric shear stress. The inflection point of the stress paths can be used to define a threshold for a rainfall-induced landslide warning system.

  2. Owing to the complexity of the relationship between external factors and landslide displacement, it is difficult to efficiently train the best landslide displacement prediction model only by manually inputting hyper-parameters, using existing machine learning methods. To overcome this setback, we proposed a nonlinear function to improve the inertia factor of particle swarm optimization (PSO). Subsequently, the hyper-parameters of the machine learning algorithm were dynamically updated by the improved particle swarm optimization. Thereafter, a landslide displacement prediction model of the improved particle swarm recurrent neural network (IPSO-RNN) based on the recurrent neural networks was proposed by fusing the rainfall and the groundwater. Finally, the proposed model was evaluated and validated using a quantity of rainfall, groundwater level, and displacement monitoring data, spanning almost 2 years, in the Heihusicun landslide, located in the Sichuan Province, China, as a case study. Based on the results obtained, compared with the traditional back propagation (BP) prediction model of landslide displacement, the proposed model can achieve a satisfactory fitting effect and maintain higher prediction accuracy.

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Authors and Affiliations

  1. Department of Cyber Security and Data Science, Riphah International University, Islamabad Campus, Islamabad, Pakistan

    Bilal Aslam & Adeel Zafar

  2. Department of Civil Engineering, COMSATS University Islamabad, Wah Campus, Wah Cant, Pakistan

    Umer Khalil

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  1. Bilal Aslam
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Aslam, B., Zafar, A. & Khalil, U. Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan. Environ Dev Sustain (2022). https://doi.org/10.1007/s10668-022-02314-6

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