Geological faults are highly developed in the eastern Liaoning Province in China, where Mesozoic granitic intrusions and Archean and Paleoproterozoic metamorphic rocks are widely distributed. Although the heat flow value in eastern Liaoning Province is generally low, the hot springs are very developed. It is obvious that the faults have significant control over the distribution of hot springs, and traditional methods of spatial data analysis such as WofE (weight of evidence) usually do not take into account the direction of the distribution of geothermal resources in the geothermal forecast process, which seriously affects the accuracy of the prediction results. To overcome the deficiency of the traditional evidence weight method, wherein it does not take the direction of evidence factor into account, this study put forward a combination of the Fry and WofE methods, Fry-WofE, based on geological observation, gravity, remote sensing, and DEM (digital elevation model) multivariate data. This study takes eastern Liaoning Province in China as an example, and the geothermal prospect was predicted respectively by the Fry-WofE and WofE methods from the statistical data on the spatial distribution of the exposed space of geothermal anomalies the surface. The result shows that the Fry-WofE method can achieve better prediction results when comparing the accuracy of these two methods. Based on the results of Fry-WofE prediction and water system extraction, 13 favorable geothermal prospect areas are delineated in eastern Liaoning Province. The Fry-WofE method is effective in study areas where the geothermal distribution area is obviously controlled by the fault. We provide not only a new method for solving the similar issue of geothermal exploration, but also a new insight into the distribution of geothermal resources in Liaoning Province.

Decision-making departments need more detailed and timely data in order to meet the needs of emergency response. Sichuan province is an area that frequently suffers natural disasters, and many disasters are caused by rainfall. This study establishes a decision support system (DSS) based on geoprocessing (GP) services, which can locate the region that overran the rainfall threshold and provide the population or property analysis, query, map plot, and path analysis functions. Most of the functions of the system are developed on the basis of geoprocessing services. This paper uses the warning–judgment module as an example to introduce the structure and function of the DSS system. The system satisfies the demands of real-time data acquisition, calculation, analysis, and presentation.