V. G. Gitis

A Systems Approach to Modeling Catastrophic Risk and Insurability

This paper describes a spatial-dynamic,stochastic optimization model that takes account ofthe complexities and dependencies of catastrophicrisks. Following a description of the general model,the paper briefly discusses a case study of earthquakerisk in the Irkutsk region of Russia. For this purposethe risk management model is customized to explicitlyincorporate the geological characteristics of theregion, as well as the seismic hazards and thevulnerability of the built environment. In its generalform, the model can analyze the interplay betweeninvestment in mitigation and risk-sharing measures. Inthe application described in this paper, the modelgenerates insurance strategies that are lessvulnerable to insolvency.

Catastrophe chains : Hazard assessment

A mathematical model describing mutually affecting catastrophes is suggested. The hazard and risk are estimated.

An approach to the automatic prediction of earthquakes

A new approach to automatic prediction of earthquakes is considered. The seismotectonic processes are represented using spatio-temporal fields. The parameters of prediction by several fields are the linear predictive function, the value of the alarm decision threshold, as well as the size of the spatio-temporal alarm domain. The considered approach is not bound to a specific data type. Simulation results show that the automatic prediction is available with the use of the Internet catalogues of earthquakes, feasible and rather efficient.

Adaptive estimation of seismic parameter fields from earthquake catalogs

A new approach to estimation of the parameters of inhomogeneous spatio-temporal marked point fields is considered. The approach is based on the adaptive weights smoothing (AWS) method. A generalized version of the ASW method for constructing spatial and spatiotemporal fields of density, mean values, and correlation (fractal) dimension of marked point fields is proposed. It is shown that the method can be used in constructing fields of seismic-process parameters from earthquake catalogs.

Mathematical modeling of the pollutants overland flow and transport

Modeling of the natural and technogenic processes in diverse geomorphological environments is one of the basic tools for forecasting and preventing unfavorable development of the urban ecology. One of the causes of its deterioration lies in pollution. The paper considers mathematical modeling of the spread of pollutants transported with water. The complicated process of pollutant spread was modeled as an aggregate of four simpler models such as overland water flow, influent seepage, pollutants transport with surface runoff, and pollutant deposition (accumulation) on the land surface. The model relies on the diffusion equation with supplementary addends in the right-hand side of which one reflects the effect of the terrain relief and the other, which depends on the lithologic structure of the territory, defines the intensity of pollutant uptake rate by the land surface. This equation is satisfied in the two-dimensional domain corresponding physically to an area covered with water. Both the form of the boundary and topology of this area are time-dependent because of appearance of dry “islands” surrounded by water.

Features of geonomic prognostic functions for the maximum possible earthquake

The recent artificial intelligence techniques are commonly applied to solving problems in which multidimensional statistical analyses of various quantities and their modelling prognostic functions can be used. This paper attempts to summarize the characteristics of the prognostic functions applied in the determination of the maximum possible earthquake. Geonomic quantities are reviewed and categorized with respect to their influence upon the estimation of the maximum possible earthquake.

Earthquake Prediction Learning Using the Least Alarm Method

An approach to organization of a system for automatic earthquake prediction is developed. This approach is not bound to any particular type of data, and all data on seismotectonic processes are displayed using the spatial and space-time fields. The prediction field is learned from the feature fields and the sample of predicted earthquakes. The learning method proposed here is the least alarm method. The results of modeling of earthquake prediction for the regions of Japan and Mediterranean are presented. Modeling implied the analysis of prediction fields reflecting stationary and dynamic properties of the seismic process. The results of modeling demonstrate the efficiency of application of the least alarm method to earthquake prediction.

Technology of Monitoring and Analysis of the Hydrometeorological Situation in the Arctic

A new geoinformation technology for monitoring the hydrometeorological situation in the Arctic is considered. This technology combines two levels of the geodata analysis. The first level supports automatic loading and processing of data, providing the operator with simple tools of the analysis with an intuitive interface and visual representation of results available to a wide range of users. The second level is designed for the comprehensive analysis of hypotheses that experts can formulate at the first level. The monitoring platform has two software applications: (1) climatic processes in the Arctic and (2) operative and forecast conditions in the regions of the White and Barents seas with the possibility of analyzing the environmental threats during extraction and transportation of hydrocarbons.

Prediction of the Aggressive Status of Prostate Cancer on the Basis of Preoperative Data

The problem concerning the prediction of the aggressive status of prostate cancer (PCa) is examined on the basis of preoperative data. This problem is solved using data on 360 patients with the established (aggressive or indolent) postoperative status of the disease. The collection of factors containing five informative indicators of prediction (from primarily accessible sixteen) is revealed and employed to create the diagnostic index used to predict the aggressive PCa status. In compliance with cross-validation data, the prognostic algorithm enables us to find the group involving 55% of patients with an aggressive status in the absence of patients with an indolent PCa status. The three-class prediction algorithm making it possible to ascertain whether any patient belongs to the group with the low, high, or uncertain risk of the aggressive disease stage is proposed.

Индекс ВИЗГ – новый мультипараметрический показатель для рака предстательной железы

Serum of 336 patients with primary prostate cancer (PC) with baseline total prostate-specific antigen level (totPSA) < 30.0 ng/ml was tested for free PSA (freePSA) and [-2]proPSA; %freePSA, %[-2]proPSA, prostate health index (phi), and a new index APHIG calculated using lab tests and taking into account age, T stage and Gleason score from biopsy were evaluated. Obtained data was compared to tumor stage (pTNM) and malignancy grade according to the Gleason score based on the final histological report after prostatectomy. APHIG has statistically significant benefits compared to PSA-associated markers for differentiation of clinically significant subgroups of PC: pT2c/pT3a/pT3b; local indolent PC/local aggressive/locally advanced/PC with regional metastases; total Gleason score 5–6/7(3 + 4)/7(4 + 3).