Integrated risk assessment of complex disaster system based on a non-linear information dynamics model

This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster system with complicated and nonlinear characteristics, a non-linear information dynamics mode is introduced based on the maximum flux principle during modeling process to study the integrated risk assessment of complex disaster system. Based on the non-equilibrium statistical mechanics method, a stochastic evolution equation of this system is established. The integrated risk assessment of complex disaster system can be achieved by giving reasonable weights of each evaluation index to stabilize the system. The new model reveals the formation pattern of risk grade and the dynamics law of evolution. Meanwhile, a method is developed to solve the dynamics evolution equations of complex system through the self-organization feature map algorithm. The proposed method has been used in complex disaster integrated risk assessment for 31 provinces, cities and autonomous regions in China mainland. The results have indicated that the model is objective and effective.

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

This paper describes a building subsidence deformation prediction model with the self-memorization principle.According to the non-linear specificity and monotonic growth characteristics of the time series of building subsidence deformation,a data-based mechanistic self-memory model considering randomness and dynamic features of building subsidence deformation is established based on the dynamic data retrieved method and the self-memorization equation.This model first deduces the differential equation of the building subsidence deformation system using the dynamic retrieved method,which treats the monitored time series data as particular solutions of the nonlinear dynamic system.Then,the differential equation is evolved into a difference-integral equation by the self-memory function to establish the self-memory model of dynamic system for predicting nonlinear building subsidence deformation.As the memory coefficients of the proposed model are calculated with historical data,which contain useful information for the prediction and overcome the shortcomings of the average prediction,the model can predict extreme values of a system and provide higher fitting precision and prediction accuracy than deterministic or random statistical prediction methods.The model was applied to subsidence deformation prediction of a building in Xi'an.It was shown that the model is valid and feasible in predicting building subsidence deformation with good accuracy.