Abstract

As global greenhouse gas concentrations intensify climate change impacts, the risk of landslides increases, particularly in Southern Anambra State, Nigeria. This ongoing threat endangers lives, farmlands, and property, emphasizing the need to pinpoint susceptible areas for effective prevention and mitigation strategies. Employing four classical statistical models—frequency ratio (FR), Shannon's entropy (SE), the weight of evidence (WoE), and logistic regression (LR)—this study identified classes within conditioning factors contributing to landslide formation. The research also evaluated and contrasted the accuracy of these models, considering their combined application, which remained unexplored. Using high-resolution spatial data, twelve conditioning factors and landslide inventory datasets, divided into training (80%) and testing (20%), susceptibility maps, accuracy, and errors were generated for all the statistical models. All models exhibited good accuracy, with slightly increased error margins within an acceptable range. Susceptibility maps generated highlighted the central region as highly landslide-prone, influenced by geological factors (poorly consolidated formations), slope (> 12.253°), elevation (212 to 328 m), rainfall (516.4 to 585.3 mm), distance to the stream (< 111.7 to 223.4 m), land cover (crops and rangeland), NDVI (< 0.201), and SPI (> 1.827). Comparison of the obtained statistical results revealed similarities and differences in accuracy and model performance; as inconsistencies exist with previous studies, suggesting that although geospatial characteristics influence landslide susceptibility studies, the controlling factors for landslide formation are not universally exclusive. The insights provided by this paper are valuable for decision-makers involved in hazard monitoring and management efforts.

中文翻译：

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使用经典统计模型对尼日利亚阿南布拉州南部滑坡易发区进行地理空间评估

摘要

随着全球温室气体浓度加剧气候变化影响，山体滑坡的风险增加，特别是在尼日利亚阿南布拉州南部。这种持续的威胁危及生命、农田和财产，强调需要查明易受影响的地区，以采取有效的预防和缓解策略。本研究采用四种经典统计模型——频率比（FR）、香农熵（SE）、证据权重（WoE）和逻辑回归（LR）——确定了导致滑坡形成的调节因素的类别。该研究还评估和对比了这些模型的准确性，考虑到它们的组合应用，这方面仍有待探索。使用高分辨率空间数据、十二个调节因素和滑坡清单数据集，分为训练（80%）和测试（20%），为所有统计模型生成了敏感性图、准确性和误差。所有模型都表现出良好的准确性，误差范围略有增加，在可接受的范围内。生成的易发性地图突出显示中部地区高度滑坡，受地质因素（固结不良）、坡度（> 12.253°）、海拔（212 至 328 m）、降雨量（516.4 至 585.3 mm）、距河流距离的影响（< 111.7 至 223.4 m）、土地覆盖（农作物和牧场）、NDVI (< 0.201) 和 SPI (> 1.827)。比较获得的统计结果揭示了准确性和模型性能的异同；由于与以前的研究存在不一致，这表明虽然地理空间特征影响滑坡敏感性研究，但滑坡形成的控制因素并不具有普遍的排他性。本文提供的见解对于参与危害监测和管理工作的决策者来说很有价值。