气象信息网站后台管理技术研究

对当前气象信息网站进行概述,就气象网站后台管理的开发应用从结构和技术上进行了分析。     

地震次生火灾可扑救性的网络分析

以忻州市为例,选取其中３个次生灾害源,结合城市供水、建筑、交通等系统,提出用网络分析法研究地震次生火灾的可扑救性．     

WebGIS技术在地震应急指挥信息系统中的应用

从GIS的最新发展技术WebGIS的概念出发,研究了目前使用WebGIS的几种常见的开发方式,分析了在地震应急指挥信息系统中采用WebGIS技术对地震灾害应急指挥带来的优势,同时探讨了地震信息采集、信息处理、系统集成发布以及地震灾害评估等一系列问题的处理过程。设计研究了基于WebGIS技术建立地震应急指挥系统的理论实现过程和集成实现方案。将WebGIS技术应用在地震应急指挥信息系统中,也为处理其他灾害防御指挥信息系统提出了一种新的解决问题的途径。     

The impact of the Sustainable Development Goals on a network of 276 international organizations

Global sustainability governance is marked by a highly fragmented system of distinct clusters of international organizations, along with states and other actors. Enhancing inter-organizational coordination and cooperation is thus often recognized as an important reform challenge in global sustainability governance. The 17 Sustainable Development Goals, agreed by the United Nations in 2015, thus explicitly aim at advancing policy coherence and institutional integration among the myriad international institutions. Yet, have these goals been effective in this regard? We assess here the impact of the Sustainable Development Goals on the network structure of 276 international organizations in the period 2012–2019, that is, four years before and four years after the launch of the Sustainable Development Goals. The network structure was approximated by analyzing data from the websites of these 276 international organizations that were joined by more than 1.5 million hyperlinks, which we collected using a custom-made web crawler. Our findings are contrary to what is widely expected from the Sustainable Development Goals: we find that fragmentation has in fact increased after the Sustainable Development Goals came into effect. In addition, silos are increasing around the 17 SDGs as well as around the social, economic, and environmental dimensions of sustainable development.     

面向对象与卷积神经网络模型的GF-6 WFV影像作物分类

GF-6WFV影像是中国首颗带有红边波段的中高分辨率8波段多光谱卫星的遥感影像,对于其影像及红边波段对作物分类影响的研究利用亟待展开.本文结合面向对象和深度学习提出一种适用于GF-6 WFV红边波段的卷积神经网络(RE-CNN)遥感影像作物分类方法.首先采用多尺度分割和ESP工具选择最佳分割参数完成影像分割,通过面向对...     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.