African Centre of Excellence in Internet of Things, University of Rwanda, Kigali 3900, Rwanda.
School of ICT, The Copperbelt University, Kitwe 21692, Zambia.
Int J Environ Res Public Health. 2020 Jun 10;17(11):4147. doi: 10.3390/ijerph17114147.
Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model's performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model's incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data.
滑坡属于自然发生、不可预测且极具干扰性的灾害。因此，此类灾害的早期预警系统可以提醒人们并拯救生命。一些最新的早期预警模型利用物联网监测环境参数以预测灾害。其他一些模型则使用机器学习技术 (MLT) 分析降雨量数据以及一些内部参数来预测这些灾害。现有模型和系统的预测能力在准确性方面受到限制。在本研究论文中，提出了两种预测建模方法，即随机森林 (RF) 和逻辑回归 (LR)。这些方法使用降雨数据集以及各种其他内部和外部参数进行滑坡预测，从而提高准确性。此外，还使用前期累积降雨数据进一步提高了这些方法的预测性能。使用接收者操作特征、曲线下面积 (ROC-AUC) 和假阴性率 (FNR) 来评估这些模型，以衡量未报告的滑坡情况。当包含前期降雨数据时，两种模型 (RF 和 LR) 的表现都更好，AUC 分别为 0.995 和 0.997。结果表明，前期降水与滑坡发生之间存在很好的相关性，而不是一天的降雨与滑坡发生之间的相关性。在错误预测方面，RF 和 LR 将 FNR 分别提高到 10.58%和 5.77%。还注意到，在用于预测的各种内部因素中，坡度角的影响比其他因素高。比较两种模型，LR 模型在 FNR 方面的性能更好，因此可以优先用于滑坡预测和预警。LR 模型的错误预测率 FNR = 9.61%，不包括前期降水数据，包括前期降水数据时为 3.84%。
