Abstract Landslides are among the many devastating natural calamities that cause damage to life and property. Predicting landslides is an important task to enable preventive measures to be made on time. This paper presents an analysis of univariate time-series forecasting data using an auto regressive integrated moving average (ARIMA) model, a generalized autoregressive conditional heteroskedasticity (GARCH) model, and a dynamic neural network (DNN) model. These techniques rely on the objective of the forecasting, the type of forecasted data, and whether an automatic or manual approach is to be used for forecasting. Different techniques were analyzed on 15-meter landslide sensor data. The objective of this paper is to suggest a best method among well-known models for landslide forecasting. The demonstrated result shows that a dynamic neural network model is best in class for time-series landslide forecasting. Furthermore, upon objectively evaluating the three well-known techniques, the DNN model exhibited a minimum error rate of approximately 0.01 in comparison to other implemented techniques.

中文翻译：

---

使用各种时间序列预测模型进行滑坡数据分析

摘要 山体滑坡是造成生命财产损失的众多毁灭性自然灾害之一。预测滑坡是及时采取预防措施的一项重要任务。本文介绍了使用自回归积分移动平均 (ARIMA) 模型、广义自回归条件异方差 (GARCH) 模型和动态神经网络 (DNN) 模型对单变量时间序列预测数据进行的分析。这些技术取决于预测的目标、预测数据的类型以及是使用自动还是手动方法进行预测。对 15 米滑坡传感器数据分析了不同的技术。本文的目的是在众所周知的滑坡预测模型中提出一种最佳方法。演示的结果表明，动态神经网络模型是同类时间序列滑坡预测的最佳选择。此外，在客观评估这三种众所周知的技术后，与其他实施的技术相比，DNN 模型的最小错误率约为 0.01。