Two judging criteria to check validity of a model for filling gaps caused by incomplete geospatial data.

Many models can be used to fill the gaps caused by incomplete geospatial data. But not all are valid. To study the validity of geospatial information diffusion model, in this article, two judging criteria are suggested to check if a model is valid for filling a gap unit. The root mean squared error of a model with a given sample after removing a test point is called datum error of the model. The error between real value and estimated value of the test point is called forecasting error of the model. The first criterion says that, when the average forecasting error is less than the average datum error, the model is invalid. The second criterion says that, the smaller the errors, the more valid the model. The results of computer simulation show that geospatial information diffusion model is more valid than the geographically weighted regression and the back propagation neural network.

Comprehensive disposal plan design & polymerization disaster risk assessment based on information diffusion technology in Fujiang River basin in Sichuan.

The families' data damaged by the earthquake and flood in villages and towns along Fujiang River in Sichuan are collected by using intelligences Internet of, and the process and the degree of ruined the production and life of the farmers in this area are evaluated through the information diffusion technology. The villages and towns had suffered a large loss of integrated risk from floods and earthquakes, with an average annual loss of 5.758 million Yuan. Through the comprehensive suppression and blocking intervention combined with the village watershed management and post-disaster reconstruction, the frequency and the degree of disaster occurrence have gradually decreased in the past decades, and the resilience of infrastructure such as regional rivers and diversion canals to resist integrated disasters have been enhanced and the effect of this design scheme of comprehensive disaster prevention and controlling is remarkable.

An information diffusion technique to assess integrated hazard risks.

An integrated risk is a scene in the future associated with some adverse incident caused by multiple hazards. An integrated probability risk is the expected value of disaster. Due to the difficulty of assessing an integrated probability risk with a small sample, weighting methods and copulas are employed to avoid this obstacle. To resolve the problem, in this paper, we develop the information diffusion technique to construct a joint probability distribution and a vulnerability surface. Then, an integrated risk can be directly assessed by using a small sample. A case of an integrated risk caused by flood and earthquake is given to show how the suggested technique is used to assess the integrated risk of annual property loss.

A Risk Radar driven by Internet of intelligences serving for emergency management in community.

Today, most of the commercial risk radars only have the function to show risks, as same as a set of risk matrixes. In this paper, we develop the Internet of intelligences (IOI) to drive a risk radar monitoring dynamic risks for emergency management in community. An IOI scans risks in a community by 4 stages: collecting information and experience about risks; evaluating risk incidents; verifying; and showing risks. Employing the information diffusion method, we optimized to deal with the effective information for calculating risk value. Also, a specific case demonstrates the reliability and practicability of risk radar.

Fuzzy risks and an updating algorithm with new observations.

Concerning the essence of risk, we suggest a new definition of risk: a scene in the future associated with some adverse incident. In many cases, risks are rather fuzzy for our perception because of the shortage of knowledge or information about the systems that determine the adverse incidents. We introduce a concept of fuzzy risk based on the new risk definition and fuzzy sets. And, in this article, we suggest a fuzzy average algorithm to update a fuzzy risk that stores all information from the original data. To illustrate the algorithm, we update a soft risk map of flood where the fuzzy risks are calculated using the interior-outer-set model.

Using magnetic seeds to improve the aggregation and precipitation of nanoparticles from backside grinding wastewater.

Backside grinding (BG) wastewater treatment typically requires large quantities of chemicals, i.e. polyaluminum chloride (PAC) coagulant and produces considerable amounts of sludge, increasing the loading and cost of subsequent sludge treatment and disposal processes. This study investigated the effects of the addition of magnetic seeds (FeO*Fe(2)O(3)) of selected particle sizes and of optimized combinations of magnetic seeds and PAC on the aggregation of silica nanoparticles from BG wastewater and on the sedimentation time at various pH values. The results show that the turbidity of BG wastewater was significantly reduced by the magnetic aggregation treatment. The dosage of PAC combined with 2.49gL(-1) or 1.24gL(-1) of magnetic seeds was reduced by 83% (from 60 to 10mgL(-1)) compared to the conventional process of using only PAC as a coagulant. The turbidity of the BG wastewater, initially 1900-2500NTU, could also be successfully decreased about to 23NTU by the addition of 3.74gL(-1) magnetite (FeO*Fe(2)O(3)) only at pH 5 with an applied magnetic field of 1000G. Different coagulation conditions using magnetic seeds combined with coagulant resulted in different aggregation performances. The treatment performance was more effective by using two-stage dosing, in which magnetic seeds and PAC were added separately, than that with one-stage dosing, where the magnetic seeds and PAC were added simultaneously during rapid mixing. The two-stage dosing allowed for a reduction in the optimum dosage of magnetic seeds from 3.74gL(-1) to 2.49gL(-1) or 1.24gL(-1) without affecting performance when coupled with 0.01gL(-1) of PAC coagulant. The developed method effectively reduced the production of waste sludge.