Landslide catastrophes and disaster risk reduction: A GIS framework for landslide prevention and management

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning. Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors. Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. In this review paper, 77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined. We analyze the structures of different DL networks, discuss on five main application scenarios, and assess both the advancements and limitations of DL in geological hazard analysis. The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence, with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization. Finally, we explored the hindrances of DL in landslide hazard research based on the above research content. Challenges such as black-box operations and sample dependence persist, warranting further theoretical research and future application of DL in landslide detection.     

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity

ABSTRACT

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.     

Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network

Modelling the mechanical behaviour of unsaturated soils has been the subject of many research works in the past few decades. A number of constitutive models have been developed to describe the complex behaviour of unsaturated soils. Despite the significant advances in the constitutive theories for unsaturated soils, none of the existing models can completely describe the various aspects of the real behaviour of unsaturated soils. In this paper, a new unified approach is presented, based on the integration of a neural network and a genetic algorithm, for the modelling of unsaturated soils. In the proposed approach, a genetic algorithm was used to optimise the weights of the neural network. A three-layer sequential architecture was chosen for the neural network. The network had eight input neurons, five neurons in the hidden layer and three neurons in the output layer. The eight input neurons represented the initial gravimetric water content, initial dry density, degree of saturation, net mean stress with respect to pore-air pressure, axial strain, deviatoric stress, soil suction and volumetric strain, and the three neurons in the output layer represented the deviatoric stress, suction and volumetric strain at the end of each increment. The network was trained and tested using a database that included results from a comprehensive set of triaxial tests on unsaturated soils from the literature. The predictions of the proposed model were compared with the experimental results. The comparison of the results indicates that the proposed approach was accurate and robust in representing the mechanical behaviour of unsaturated soils.     

地图符号识别的神经网络模型

着重讨论地图符号识别的神经网络模型。分析了神经网络的信息处理和信息加工方式,讨论了神经网络的设计要点和基本模型,以及神经网络的分类器。最后,还给出了几种常用的网络模型。     

建筑物变形短期预测的神经网络方法

从系统论出发,把建筑物变形看成一个黑箱系统,提出了利用神经网络对建筑物变形进行短期预测的一种新方法,在此方法中,利用多层前馈神经网络实现系统的输出与输入映射,采用正的反向传播算法作为学习算法。用实例加以证明,其拟合和预测的结果都令人满意。     

人工神经网研究的剖析与探讨

本文概述了人工神经网与生物神经网研究的关系．给出了确立人工神经网中的基本原理和模式的生物神经网的依据。分析了人工神经网在各种意义下的分类及研究的基本内容。指出了目前若干研究动向和前沿性课题。     

Neurofuzzy mapping of CPT values into soil dynamic properties

Ever since the early days of soil mechanics there has been a strong tendency to develop correlations between easy-to-obtain parameters and mechanical properties of geotechnical materials. This interest has not decreased as more elaborated methods have been proposed. On the contrary, once a method under development reaches certain degree of sophistication, researchers usually devote a great deal of time to establish simplified procedures. The study presented in this paper follows a similar path in the sense that a data-based knowledge procedure instead of mathematically based methods is used to develop a manner to estimate soil dynamic properties directly from cone-tip penetration resistances. This method is based on a hybrid system integrated by merging artificial neural networks and fuzzy logic. The procedure is evaluated comparing its results with actual measurements of cone-tip penetration resistances and shear wave velocities done in twin borings.     

基于GEP的桩顶水平位移预测模型研究

桩顶水平位移的变形监测是一项重要内容,针对传统预测方法存在的不足,本文采用基因表达式编程(GEP),利用Eclipse平台下的Java编程语言,建立了桩顶水平位移预测模型。将灰色GM(1,1)模型、BP神经网络模型和基因表达式编程(GEP)这三种模型对桩顶水平位移的预测结果进行对比分析,得出基因表达式编程不仅能够提高桩顶水平位移的预测精度,而且其学习效率比灰色GM(1,1)模型和BP神经网络模型也有显著提高,从而证明基因表达式编程模型在桩顶水平位移预测方面具有可行性。     

基于神经网络的高速公路软基沉降分析

简述了高速公路软基沉降观测的目的和意义。介绍了工后沉降的预测方法。其后，简述了BP神经网络的基本概念。论文提出了基于神经网络的高速公路工后沉降预报方法。结合江苏省某高速公路现场监测资料，进行了实例分析，说明此方法是可行的和有效的。它对提高高速公路建设质量具有十分重要的现实意义。     

神经网络方法在爆炸地震震中定位方面的应用

地震定位过程中,由于地球介质的不均一性以及台站局部地质条件的复杂性,使震中距和地震波走时呈非线性关系。利用通常地震定位方法所确定的爆炸地震震中位置和实际震中存在２０～３０ｋｍ偏差。人工神经网络具有高度非线性映射功能,可应用于地震震中定位。应用ＢＰ（反向传播）神经网络确定远场爆破地震震中的实例表明,所确定的震中位置和实际震中位置偏差在８ｋｍ以内,外延预测确定的震中位置和实际震中位置偏差小于１８ｋｍ