Department of Engineering Management, School of Civil Engineering, Central South University, Changsha, Hunan 410083, China.
Pricing Certificate Centre, Changde Municipal Development and Reform Commission, Changde, Hunan 415000, China.
Comput Intell Neurosci. 2022 May 23;2022:9923775. doi: 10.1155/2022/9923775. eCollection 2022.
Landslides are one of the most widespread natural hazards that cause damage to both property and life every year. Therefore, the landslide susceptibility evaluation is necessary for land hazard assessment and mitigation of landslide-related losses. Selecting an appropriate mapping unit is an essential step for landslide susceptibility evaluation. This study tested the back propagation (BP) neural network technique to develop a landslide susceptibility map in Qingchuan County, Sichuan Province, China. It compared the results of applying six different slope unit scales for landslide susceptibility maps obtained using hydrological analysis. We prepared a dataset comprising 973 historical landslide locations and six conditioning factors (elevation, slope degree, aspect, lithology, distance to fault lines, and distance to drainage network) to construct a geospatial database and divided the data into the training and testing datasets. We based on the BP learning algorithm to generate landslide susceptibility maps using the training dataset. We divided Qingchuan County into six different scales of slope unit: 4,401, 13,146, 39,251, 46,504, 56,570, and 69,013, then calculated the receiver operating characteristic (ROC) curve, and used the area under the curve (AUC) for the quantitative evaluation of 6 different slope unit scales of landslide susceptibility maps using the testing dataset. The verification results indicated that the evaluation generated by 56,570 slope units had the highest accuracy with a ROC curve of 0.9424. Overelaborate and rough division of slope units may not get the best evaluation results, and it is necessary to obtain the slope units most consistent with the actual situation through debugging. The results of this study will be useful for the development of landslide hazard mitigation strategies.
滑坡是最广泛的自然灾害之一，每年都会造成财产和生命损失。因此，滑坡易发性评价对于土地灾害评估和减轻滑坡相关损失是必要的。选择合适的制图单元是滑坡易发性评价的重要步骤。本研究测试了反向传播（BP）神经网络技术，以开发中国四川省青川县的滑坡易发性图。它比较了应用六种不同坡度单元尺度进行滑坡易发性图的结果，这些结果是通过水文分析获得的。我们准备了一个包含 973 个历史滑坡位置和六个条件因素（海拔、坡度、方位、岩性、距断层线的距离和距排水网络的距离）的数据集，以构建一个地理空间数据库，并将数据分为训练和测试数据集。我们基于 BP 学习算法，使用训练数据集生成滑坡易发性图。我们将青川县分为六个不同尺度的坡度单元：4401、13146、39251、46504、56570 和 69013，然后计算接收者操作特征（ROC）曲线，并使用曲线下面积（AUC）对 6 种不同坡度单元尺度的滑坡易发性图进行定量评价，使用测试数据集。验证结果表明，56570 个坡度单元生成的评价具有最高的精度，ROC 曲线为 0.9424。过于细致和粗略的坡度单元划分可能无法获得最佳评价结果，有必要通过调试获得最符合实际情况的坡度单元。本研究的结果将有助于制定滑坡灾害缓解策略。
