Research On Landslides Early Warning Based On Parallel Coordinate Visualization

Geological disasters not only threaten human life,but also on the human living environment and resources cause enormous damage, landslides occurred most frequently in which the impact is extremely bad. Studies found that there many factors affecting landslide and have a lot of uncertainty, every factor keep change and difficult to find a uniform law. Therefore it is difficult to make the induction for the law as well as the occurrence of landslides in an area of landslide stability evaluation. Meanwhile, stability evaluation depend on high dimensional, non-normal and nonlinear data,while the conventional evaluation methods for these data is not strong, it is difficult to obtain the desired results. For landslide data analysis and extraction of useful potential law, is researchers’ aim. During the formation mechanism of landslide physical process, the landslide process is quite complex and difficult to accurately represent what is to be preceded by landslide injuries caused further deterioration resulting in life, damaged housing and public facilities.Currently, the landslide data mining has become a researcher’s work focus and direction. As for those monitoring systems become more perfect and good placement of communications equipment for real-time monitoring data, the researchers demonstrated a strong interest. Different data mining algorithms are applied to these data, get a lot of valuable information, but the algorithm complexity and computationally intensive characteristics of mass data obtained from different data sources it is difficult to be understood and more good use of. Data mining and visualization technology have been made to solve these problems, organically together to achieve the two technologies complement each other, this integration makes data mining technology to be more intuitive and graphically display, it organically integrates people’s cognitive abilities, creativity, different fields of knowledge and data mining process, and give full play to the advantages of both, not only reflects the central role of the user, but also improves the efficiency and accuracy of data mining.For the stability of the landslide affected by many complex factors, and the data collected have the features of not-comprehensive or redundant, in order to establish a stable indicator system, this paper first quantified the factor that affecting the stability of the landslide, then simplified and deleted the properties of those unwanted items. For a variety of deficiencies of data analysis algorithms and predictive models in different landslide stability evaluation, this paper discussed the thinking of establishing landslide early warning and forecast analysis model based on the basic principles of visual data mining and the features of parallel coordinates visual data mining, and applied this method in landslide stability evaluation. Then did combined analysis using a variety of landslide monitoring data and historical data, and using the parallel coordinate axes brush technology, switching axes, up-scroll and drill-down, abstract and other analytical methods to identify the associated data items, through using direct cluster analysis and interactive clustering to discover the implicit relationships and associated factors between two parallel coordinate axes. Ultimately, we combined data mining method of parallel coordinates visualization with clustering analysis, completed the design for landslide forecasting and early warning modeling. At the same time, in view of mathematical foundation and mapping rules of parallel coordinates visualization method, we discusse and develope the evaluation grade standard of landslide forecast and early warning based on parallel coordinates visualization.In order to illustrate that this method is applicable to a variety of regional landslide, reflect the general characteristics of landslide early warning model, we selected some previous landslide monitoring data, historical disaster and hidden danger investigation data for analysis. Experiments show that the parallel coordinates visualization method give get full play to analyze these data. It is not only has the important instruction function to the establishment of the landslide monitoring system and evaluation index system, it also establishes the early warning model, which is easily to be operated and understood by non-professional users, contributed to disaster prevention and mitigation.This article applied simple and easy to accept parallel coordinate visualization method to landslide monitoring and early warning analysis modeling, not only achieved all real-time visualization of landslide monitoring data, but also achieved a vision of space-time series analysis simultaneous. The results of experiments shows that this method is suitable for different regions of different types of landslide analysis and modeling, not only simplifies the mathematical modeling process, saves much computing time for severing people, but also making the analysis of the modeling process in complete visualization and easier to understand, then laid a solid foundation for relevant departments to make timely decisions to prevent disaster. This method can improve the speed of landslide modeling, upgrade the accuracy and efficiency of forecasting and warning, and then save human lives and property.