## 5 Conclusions  
Landslides are one of the most influential environmental challenges due to transiting a large amount of sediment to the closest stream, and loss of life and property as well. Therefore, landslide susceptible maps are the most practical tool to landslide mitigation. In this work, we introduce two novel hybrid machine leaning methods for LSM. Specifically, the ANFIS-based technique was improved by being combined with WOA, GWO, and PSO, and three proposed SWARA-ANFIS-WOA, SWARA-ANFIS-GWO and SWARA-ANFIS-PSO methods are introduced and the result is compared with SWARA and SWARA-ANFIS without any optimization process. For the proposed framework, the landslide inventory map of the study area is first compiled, and the influencing factors are selected. Then, the SWARA algorithm is used to obtain the initial weight of each class of landslide condition factors. Next, the landslide modelling is conducted on the study area using SWARA-ANFIS-WOA, SWARA-ANFIS-GWO, and SWARA-ANFIS-PSO. Finally, the landslide susceptibility maps are assessed using several objective measures. In general, the conclusion of this can be summarized as follows:  
1. SWARA-ANFIS-GWO has the best result in terms of the convergence of objective function in comparison to SWARA-ANFIS-WOA.
2. SWARA-ANFIS-GWO outperforms SWARA-ANFIS-WOA and thus is a better tool for landslide susceptibility modelling.  
3 The hybrid SWARA-ANFIS-GWO method has better goodness-of-fit and prediction accuracy and can produce more accurate reliable results. Therefore, it is recommended for LSM in other dangerous areas.
4 In the study area, the landslides are prone to occur under conditions of slop with \(>\)47.6\({}^{\circ}\), south aspect, altitude with 882-1032 m, plan curvature with \(>\)0.001, profile curvature with \(<\)\(-\)0.001, SPI with 800-1000, TWI with 3-5, lithology of Lu Jing Unit, distance to fault with 0-500 m, distance to river with 600-750 m, forest land-uses, NDVI with 0.35-0.45, soil of ACu and 1500-1600 mm rainfall.  
Therefore, we recommend the ANFIS-GWO hybrid method as a promising technique to be used in other areas with more caution. It should be noted that the results of this study were obtained for the study area, which proposes that further studies should be considered in other areas with different geomorphological and geological characteristics. Since there are some uncertainties such as landslide inventory map, methods, raster resolution, and sample size, further attention should be paid to achieve reliable landslide susceptibility maps in regional scales.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed equally to the work. [PERSON], [PERSON], [PERSON] and [PERSON] collected field data and conducted the landslide susceptibility mapping and analysis. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote and revised the manuscript. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] provided critical comments in planning this paper and edited the manuscript. All the authors discussed the results and edited the manuscript.  
This research was supported by the National Natural Science Foundation of China (41807192, 61271408), Natural Science Basic Research Program of Shanxi (Program No. 2019 JLM-7, Program No. 2019 JQ-094), project funded by Key Laboratory of Mine Geological Hazard Mechanism and Control, Shaanxi Provincial Natural Resources Department (Program No. KF2018-05), and Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.17H84).  
The authors declare no conflict of interest.