## 1 Introduction  
Landslides are a ubiquitous global hazard [1] posing significant threats to life and property. The statistics data show that landslide disasters affected 5 million people and caused total damage of 4.7 billion US dollars during the period from 2000 to 2020 [2]. As shown in Figure 1, China, the USA, Japan, Nepal, and India are the most landslide-prone regions [3], among which China suffers the most landslide disasters. In the past two decades, landslides have killed 3706 people and caused over 2 billion US dollars of estimated damage to China. Landslide early warning has proven to be the most effective measure for landslide mitigation [4; 5], and landslide displacement prediction has been catching extensive attention from practitioners and scholars because of its significant importance in early landslide warning systems [6; 7]. However, due to the inherent nonlinear characteristics of landslide monitoring data, achieving the desired prediction accuracy remains highly difficult and challenging. Therefore, it is essential to develop an effective and accurate prediction model to improve the performance of landslide displacement prediction, thus aiding landslide mitigation. A variety of landslide displacement prediction models have been proposed since the pioneering work of [PERSON] [8]. These prediction models generally fall into two main groups: Physics-based models and data-driven models [9]. Physics-based models generally require a clear understanding of the physical processes that involve a large amount of input, sophisticated mathematical tools, and significant user expertise. Therefore, the generalization ability of physics-based models is limited [4]. Recently, data-driven models, including artificial neural networks (ANNs) [10], decision trees [4], extreme learning machines (ELMs) [11; 12], support vector machines (SVMs) [13; 14; 15], quantile regression neural networks [16], random forest (RF) [17], and kernel-based ELMs and SVMs [9; 18; 19], have attracted attention in landslide displacement prediction. These studies have demonstrated that a data-driven model is capable of providing satisfactory predictions by recognizing movement patterns in historical monitoring data and establishing a mapping between input and output displacements without the requirement of complex physical processes. Recent applications have demonstrated the feasibility of data-driven models to capture nonlinear relationships and to model landslide dynamic processes based on historical model data; however, limitations remain. First, in most data-driven models, the input variables that have an important influence on the accuracy of landslide displacement prediction [9] are selected based on a priori expert knowledge, trial and error, or linear cross-correlation [9; 12]. Nevertheless, a priori expert knowledge of landslide systems is biased [20], or not always available, or even when available, knowledge acquisition tends to be a difficult and time-consuming process. Generally, input variable selection via trial and error is a brute-force process that is computationally expensive, especially for data-driven models with large input candidates. The most commonly used linear correlation coefficients only evaluate linear correlation and cannot reveal the nonlinear relationships that are generally involved in data-driven models. Therefore, a clear need exists for a systematic input variable selection process that does  
Figure 1: Spatial distribution of landslide disasters during the period from 2000 to 2020. Each dot represents a single landslide. The insets show the total deaths and total estimated damages. (Source: [[https://public.emdat.be/data](https://public.emdat.be/data)]([https://public.emdat.be/data](https://public.emdat.be/data)), accessed on 2 December 2020). not rely on a priori expert knowledge, is computationally inexpensive, and can describe nonlinear relationships.