Evaluation of deep learning algorithms for landslide susceptibility mapping in an alpine-gorge area:a case study in Jiuzhaigou County

With its high mountains,deep valleys,and complex geological formations,the Jiuzhaigou County has the typical characteristics of a disaster-prone mountainous region in southwestern China.On August 8,2017,a strong Ms 7.0 earthquake occurred in this region,causing some of the mountains in the area to become loose and cracked.Therefore,a survey and evaluation of landslides in this area can help to reveal hazards and take effective measures for subsequent disaster management.However,different evaluation models can yield different spatial distributions of landslide susceptibility,and thus,selecting the appropriate model and performing the optimal combination of parameters is the most effective way to improve susceptibility evaluation.In order to construct an evaluation indicator system suitable for Jiuzhaigou County,we extracted 12 factors affecting the occurrence of landslides,including slope,elevation and slope surface,and made samples.At the core of the transformer model is a self-attentive mechanism that enables any two of the features to be interlinked,after which feature extraction is performed via a forward propagation network(FFN).We exploited its coding structure to transform it into a deep learning model that is more suitable for landslide susceptibility evaluation.The results show that the transformer model has the highest accuracy(86.89%),followed by the random forest and support vector machine models(84.47%and 82.52%,respectively),and the logistic regression model achieves the lowest accuracy(79.61%).Accordingly,this deep learning model provides a new tool to achieve more accurate zonation of landslide susceptibility in Jiuzhaigou County.

Land Demand and Land Use Regulation for the Building of International Tourist Island in Hainan Province

We conduct analysis on the urban construction's demand for land, tourism's demand for land, infant industry's demand for land and infrastructure's demand for land in Hainan Province, respectively, and forecast the amount of newly-added land in Hainan Province in 2015 compared to 2008. Based on regional characteristics, we analyze the main problems in land use in Hainan Province, and work out the regional land use regulation plan, to provide scientific guidance for the building of International tourist island in Hainan Province.

Application of Multi-agent Models to Urban Expansion in Medium and Small Cities: A Case Study in Fuyang City,Zhejiang Province,China

In this study,three-phase satellite images were used to define rules for the allocation of time and space in construction land resources based on a complex adaptive system and game theory.The decision behavior and rules of government agent,enterprise agent and resident agent in construction land growth were explored.A distinctive and dynamic simulation model of construction land growth was built,which integrated multi-agent,GIS technology and RS data and described the interaction among influencing agents.Taking Fuyang City in the Changjiang River Delta as an example,an assessment process for the remote sensing data in construction land and scenario planning was constructed.Repast and ArcGIS were used as simulation platforms.A simulation of the spatial pattern in land-use planning and the setting of scenario planning were conducted by using the incomplete active game,which was based on different natural,social and economic levels.Through this model,a simulation of urban planning space and decision-making for Fuyang City was created.Relevant non-structured problems arising from urban planning management could be identified,and the process and logic of urban planning spatial decision-making could thus be improved.Cel1-by-cel1 comparison showed that the simulation accuracy was over 72%.This model has great potential for use by government and town planners in decision support and technique support in the policy-making process.