Terrestrial, Atmospheric and Oceanic Sciences最新文献

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Development of landslide susceptibility mapping with a multi-variance statistical method approach in Kepahiang Indonesia 多方差统计方法在印尼吉巴兴滑坡易感性制图中的发展

4区地球科学 Q4 GEOSCIENCES, MULTIDISCIPLINARY

Terrestrial, Atmospheric and Oceanic Sciences

Pub Date : 2023-09-20 DOI: 10.1007/s44195-023-00050-6

Eli Putriani, Yih-Min Wu, Chi-Wen Chen, Arif Ismulhadi, Darmawan Ikhlas Fadli

Abstract Landslides are an example of severe natural disasters that occur worldwide and generate many harmful effects that can affect the stability and development of society. A better-quality susceptibility mapping technique for the landslide risk is crucial for mitigating landslides. However, the use of assemblages of multivariate statistical methods is still uncommon in Indonesia, particularly in the Kepahiang Regency of Bengkulu Province. Therefore, the objective of this study was to provide an improved framework for creating landslide susceptibility map (LSM) using multivariate statistical methods, i.e., the analytical hierarchy process (AHP) method, the simple additive weighting (SAW) method and the frequency ratio (FR) method. In this study, we established a landslide inventory considering 15 causative factors using the area under the curve (AUC) validation method and another evaluation technique. The performance of each causative factor was evaluated using multicollinearity and Pearson correlation analysis with regression-based ranking. The LSM results showed that the most susceptible areas were located in the districts of Kabawetan, Kepahiang, and Tebat Karai. The high landslide risk in these areas could be attributed to the slope conditions in mountainous regions, which are characterized by high annual rainfall and seismic activity. The AUC training values of the AHP, SAW, and FR methods were 0.866, 0.838, and 0.812, respectively. Then, on the validation dataset, the AHP method yielded the highest AUC value (0.863), followed by the SAW (0.833) and FR (0.807) methods. Moreover, the AHP method provided a higher accuracy value, which suggests that the AHP method is more suitable than the other methods. Therefore, our research indicated that all algorithm methods generate a positive impact and greatly improve landslide susceptibility evaluation, especially for the preparation of landslide damage assessments in this study area. Finally, the method proposed in this study could improve the feasibility of LSM and provide support for Indonesian government decision-makers in arranging hazard mitigation measures in the Kepahiang Regency, Indonesia.

滑坡是一种严重的自然灾害，在世界范围内发生，并产生许多有害影响，可以影响社会的稳定和发展。一种高质量的滑坡风险易感性制图技术对于缓解滑坡至关重要。然而，使用多元统计方法的组合在印度尼西亚仍然不常见，特别是在明古鲁省的Kepahiang县。因此，本研究的目的是利用多元统计方法，即层次分析法(AHP)方法、简单加性加权法(SAW)方法和频率比法(FR)方法，为创建滑坡易感性图(LSM)提供一个改进的框架。在本研究中，我们利用曲线下面积(AUC)验证法和另一种评价技术建立了考虑15个原因的滑坡清单。使用多重共线性和Pearson相关分析对每个致病因素的表现进行评估，并基于回归排序。LSM结果显示，最易感地区位于Kabawetan、Kepahiang和Tebat Karai地区。这些地区的高滑坡风险可能是由于山区的边坡条件，其特点是年降雨量大，地震活动频繁。AHP法、SAW法和FR法的AUC训练值分别为0.866、0.838和0.812。然后，在验证数据集上，AHP方法的AUC值最高(0.863)，其次是SAW(0.833)和FR(0.807)方法。此外，AHP方法提供了更高的精度值，表明AHP方法比其他方法更适用。因此，我们的研究表明，所有算法方法都产生了积极的影响，并大大提高了滑坡易感性评价，特别是对于本研究区滑坡危害评估的编制。最后，本研究提出的方法可以提高LSM的可行性，并为印尼政府决策者在印尼吉巴兴县安排减灾措施提供支持。

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