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With the continuous development of remote sensing(RS) technology, the surface information can be collected conveniently and quickly by using the popular unmanned aerial vehicle(UAV). The application of UAV low altitude RS technology in road safety in intelligent area has certain practical significance. It can provide safety warning for most drivers, and provide auxiliary decision-making for the road supervision department. Through the collection, processing, calculation and analysis of the road image, the UAV can find out the road obstacles with potential safety hazards, identify the road pit, calculate the radius and depth of the road pit through the digital mapping system, predict the accident risk according to different speed and provide scientific basis for the road safety monitoring. At the same time, UAV can provide repair scheme for damaged roads, estimate the quantity of materials needed for repair, and achieve the target of resource saving and efficiency improvement. The experimental results show that the UAV can not only provide scientific prediction information for driving safety, but also provide relatively accurate material consumption for road repair. [ABSTRACT FROM AUTHOR]

A two-dimensional model is presented as a way to hydrogeologically characterize the controlling factors in the Sanshandao Gold Mine. The finite element method was first applied to simulate the ground water system of the current operation, using leakage data and the calculated recharge. An inverse model was applied to the observed data (e.g., head and discharge) to verify and calibrate the ground water simulation model and to estimate the hydrogeological parameters for the water-bearing zones. Nonlinear mathematic programming was used to solve the inverse model and to estimate the model parameters in 17 districts with different hydrogeological characteristics. The finite element equations were solved by means of a large non-symmetrical sparse equation. The results were in agreement with what is currently observed in the mine. The models and the estimated parameters were then applied to predict the mine water discharge for drifts extending to depths of −330 to −600 m during the next development stage. In order to improve the predicted accuracy of the numerical model, an iterative element mesh was added in the districts near the drainage drifts so that the computed discharges that flowed into the drifts would approach the recharges that flow into the borders of the mine. The model was also used to understand how the mine discharge would be influenced by factors such as unsteady ground water flow and the construction of hydraulic barriers to restrict ground water from entering the pits. [ABSTRACT FROM AUTHOR]

The Sanshandao Gold Mine is one of the largest gold mines in China, and is almost completely surrounded by seawater. Factors such as water-controlling structures and water-bearing zones that developed in the mine were studied, based on geophysical exploration data and hydrogeological investigations of the wells and drifts of the mine. The sources of recharge (seawater, brine from surface runoff, and Quaternary water) were proportionally determined based on 18O isotopic and water quality data measured for each drainage point in the pits. Appropriate boundary conditions were defined for the recharge source of the seawater and natural runoff brine. The hydraulic connections among the water-bearing zones were also considered. This work was conducted to prepare for future mine development and allowed us to simulate anticipated ground water flow. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in mainland China. HFRS is particularly endemic in Changsha, the capital city of Hunan Province, with one of the highest incidences in China. The occurrence of HFRS is influenced by environmental factors. However, few studies have examined the relationship between environmental variation (such as land use changes and climate variations), rodents and HFRS occurrence. The purpose of this study is to predict the distribution of HFRS and identify the risk factors and relationship between HFRS occurrence and rodent hosts, combining ecological modeling with the Markov chain Monte Carlo approach. Methods: Ecological niche models (ENMs) were used to evaluate potential geographic distributions of rodent species by reconstructing details of their ecological niches in ecological dimensions, and projecting the results onto geography. The Genetic Algorithm for Rule-set Production was used to produce ENMs. Data were collected on HFRS cases in Changsha from 2005 to 2009, as well as national land survey data, surveillance data of rodents, meteorological data and normalized difference vegetation index (NDVI). Results: The highest occurrence of HFRS was in districts with strong temperature seasonality, where elevation is below 200 m, mean annual temperature is around 17.5°C, and annual precipitation is below 1600 mm. Cultivated and urban lands in particular are associated with HFRS occurrence. Monthly NDVI values of areas predicted present is lower than areas predicted absent, with high seasonal variation. The number of HFRS cases was correlated with rodent density, and the incidence of HFRS cases in urban and forest areas was mainly associated with the density of Rattus norvegicus and Apodemus agrarius, respectively. Conclusions: Heterogeneity between different areas shows that HFRS occurrence is affected by the intensity of human activity, climate conditions, and landscape elements. Rodent density and species composition have significant impacts on the number of HFRS cases and their distribution. [ABSTRACT FROM AUTHOR]