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首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
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Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area

机译：基于Keras的效果评估对不同鲁棒优化算法的浅层滑坡敏感算法在热带地区

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摘要

This research aims at investigating the capability of Keras's deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard at a regional scale. Shallow landslides at the Ha Long area (Vietnam) were selected as a case study. For this regard, set of ten influencing factors (slope, aspect, curvature, topographic wetness index, landuse, distance to road, distance to river, soil type, distance to fault, and lithology) and 193 landslide polygons were prepared to construct a Geographic Information System (GIS) database for the study area. Using the collected database, the DNN with its potential of realizing complex functional mapping hidden in the data is used to generalize a decision boundary that separates the learning space into two distinct categories: landslide (a positive class) and non-landslide (a negative class). Experimental results point out that the utilized the Keras's deep learning model with the Adam optimization and the mean squared error lost function is the best with the prediction performance of 84.0%. The performance is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree. We conclude that the Keras's deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas.
机译：该研究旨在调查Keras的深度学习模型，具有三种稳健的优化算法（随机梯度下降，根均线传播和自适应力矩优化）以及在区域规模处的滑坡危害的空间建模的两损函数。 HA Long Area（越南）的浅层滑坡被选为案例研究。对于这方面，套十大影响因素（斜坡，方面，曲率，地形湿度指数，土地使用，到达距离，到河流，土壤类型，到故障距离以及岩性的距离）和193个滑坡多边形制成构建地理学习区域的信息系统（GIS）数据库。使用收集的数据库，DNN具有实现在数据中隐藏的复杂功能映射的电位的电位来概括一个决策边界，将学习空间分成两个不同的类别：滑坡（正类）和非滑坡（负类） ）。实验结果指出，利用康拉斯的深度学习模型与亚当优化和平均平方误差丢失的功能是最佳的预测性能为84.0％。性能优于采用随机森林，J48决策树，分类树和物流模型树的所采用的基准方法。我们得出结论，Keras的深度学习模型是Landslide-Prone地区浅易感性映射的新工具。

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