GIS-based Earthquake-Triggered Landslide Hazard Zoning Using Contributing Weight Model

Earthquake-triggered landslides have aroused widespread attention because of their tremendous ability to harm people's lives and properties.The best way to avoid and mitigate their damage is to develop landslide hazard maps and make them available to the public in advance of an earthquake.Future construction can then be built according to the level of hazard and existing structures can be retrofit as necessary.During recent years various approaches have been made to develop landslide hazard maps using statistical analysis or physical models.However,these methods have limitations.This study introduces a new GIS-based approach,using the contributing weight model,to evaluate the hazard of seismically-induced landslides.In this study,the city and surrounding area of Dujiangyan was selected as the research area because of its moderate-high seismic activity.The parameters incorporated into the model that related to the probability of landslide occurrence were:slope gradient,slope aspect,geomorphology,lithology,base level,surface roughness,earthquake intensity,fault proximity,drainage proximity,and road proximity.The parameters were converted into raster data format with a resolution of 25×25m2 pixels.Analysis of the GIS correlations shows that the highest earthquake-induced landslide hazard areas are mainly in the hills and in some of the moderately steep mountainous areas of central Dujiangyan.The highest hazard zone covers an area of 11.1% of the study area,and the density distribution of seismically-induced landslides was 3.025/km2 from the 2008 Wenchuan earthquake.The moderately hazardous areas are mainly distributed within the moderately steep mountainous regions of the northern and southeastern parts of the study area and the hills of the northeastern part;covering 32.0% of the study area and with a density distribution of 2.123/km2 resulting from the Wenchuan earthquake.The lowest hazard areas are mainly distributed in the topographically flat plain in the northeastern part and some of the relatively gently slopes in the moderately steep mountainous areas of the northern part of Dujiangyan and the surrounding area.The lowest hazard areas cover 56.9% of the study area and exhibited landslide densities of 0.941/km2 and less from the Wenchuan earthquake.The quality of the hazard map was validated using a comparison with the distribution of landslides that were cataloged as occurring from the Wenchuan earthquake.43.1% of the study area consists of high and moderate hazardous zones,and these regions include 83.5% of landslides caused by the Wenchuan earthquake.The successful analysis shows that the contributing weight model can be effective for earthquake-triggered landslide hazard appraisal.The model's results can provide the basis for risk management and regional planning is.

Quantitative Assessment of Impacts of Climate and Economic-technical Factors on Grain Yield in Jilin Province from 1980 to 2008

Climate change is one of the most important challenges threatening agricultural grain yield and food security. Determining the factors influencing grain yield in Jilin Province and the weights of their contribution are a very important task, because Jilin Province is an important agriculture base in China. In this study, the accumulation factor sequence evaluating data method was used to analyze the climate and economic-technical factor contribution weights to grain yield and grain yield changes in each city of Jilin Province. Climate yield was also estimated to study the climate effect on the grain yield, and it was calculated in two ways： an improved algorithm and a traditional quadratic method. The results show that the climate and economicechnical factors have different contribution weights to grain yield in different cities in Jilin Province. The contribution weight of the climate factor to grain yield was 0.212-0.349, while that the economic-technical factor was 0.651-0.788. Furthermore, the changes of the climate factor contributing to grain yield changes accounted for 0.296-0.546, and the changes of the economic-technical factor accounted for 0.454-0.704. The weights of climate and economic-technical factor contributing to grain yield are very different between the eastern and western cities in Jilin Province, but their weights contributing to the grain yield change are similar in these cities. In general, the amount of fertilizer used per hectare （FUPH） is the main factor affecting grain yields and yield changes from 1980 to 2008. It is noted that when the FUPH growth rate stabilized after 1995, the effects of the climate factor on the grain yield become more obvious than before. The improved algorithm is effective for esti- mating climate yield in Jilin Province, and the climate yields were mostly between -500 kg/ha and 500 kg/ha, and showed a slightly rising trend in most cities.

Study on the Unequal Weight Moving Average Predition Model Based on the Neural Network

How to determine the weight value and how to determine the numbers of variables are tWo difficult questions for the inequality weight moving average forecasting model.Based n explanations of the concept of the weight contribution rate and that of the key neural node,a new method by which the weight value and the variable number can be determined has been put forward in this paper,and reality-imitating experiments have been made to prove that by way of the neural network,the difficulties existed in the traditional prediction method can be solved and the predictive precision can be improved at the same time.