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Under specific terrain and climatic conditions, it is extremely easy to cause various types of geological hazards, and the occurrence of geological hazards will affect people’s production and life, with the consequence that the economic losses are extremely great, and even severely endanger human life. However, the existing geological hazard danger zoning is slightly insufficient in accuracy and operating efficiency, and the effect in practical application needs to be further improved. In view of the above problems, this paper proposes a study on the application of GIS-based artificial neural network models in the geological disaster risk zoning. This article first expounds the related concepts of geological hazards zoning and gives the principles to be followed. Then, the calculation of evaluation factors and risk level probabilities are proposed, and a hierarchical model is constructed using the analytic hierarchy process. The weight of each evaluation factor is multiplied with the information to obtain the weighted information. Calculate the probability based on the topographic features, stratigraphic lithology, and terrain slope. Combine the artificial neural network with BP neural network to predict the results. Through the simulation experiments on the geological data of the low-mountain and hilly areas in Wanli District, Nanchang, the results show that the proportion of high-probability-prone areas and medium-probability-prone areas predicted by this paper is as high as 91.87%, and the evaluation results are consistent with the actual disaster occurrence better. The area under the ROC curve was 87.3%, which also verified the effectiveness of the method in this paper.

It is important to adopt a scientific and effective method to forecast tariffs. Using the tariff data of China from January 2014 to July 2019, the tariffs were predicted and analyzed based on the wavelet ARIMA model. Firstly, multi-scale wavelet decomposition of tariff time series is carried out, and the appropriate decomposition scale is determined and reconstructed to obtain the filtered denoising sequence. Further, the appropriate ARIMA model is established for the denoising sequence and predicted. The results show that the wavelet ARIMA model has smaller prediction error and higher precision than the conventional ARIMA model, and has good application value in the prediction of time series data.

With the development of economy, a large number of human development activities in the natural environment have directly or indirectly increased the possibility of geological disasters. Every year a large number of geological disasters pose a serious threat to the safety of human life and property, and the effective management of geological disasters has become one of the major difficulties facing our country. This paper mainly studies the risk assessment of geological hazards of the line landslide based on remote sensing and GIS. Combined with detailed survey items of geological disasters in a certain area, this paper evaluates and analyzes the distribution and development characteristics of landslide geological disasters in the study area. Disaster assessment is an important part of disaster prevention and mitigation, and provides important references for local infrastructure construction, tourism development, urban planning and regional economic development. It can be known from the experiments in this paper that the evaluation results based on the hierarchical entropy variable weight method are more accurate, and the accuracy rate is 67%. The hierarchical entropy variable weight method can not only synthesize the rich experience of experts, but also reduce the subjective impact by introducing an entropy algorithm, thereby obtaining more accurate evaluation results.

Landslide disasters are the second largest geological disaster activity in addition to earthquake disasters. They cause economic losses of up to several billion yuan each year, resulting in thousands of deaths and injuries. Therefore, it is important to monitor and alert landslide disasters. Significance. In addition, after the occurrence of landslides, a large amount of deposits will be created, which will further breed mudslides. The ability to qualitatively and quantitatively extract landslide information at the first time is particularly important for disaster prevention, mitigation, and disaster management. Therefore, the extraction of landslide information is a significant measure. At the same time, due to the limitations of sensors, how to organically fuse data from multiple sensors to achieve tracking performance that cannot be achieved by a single sensor has become the focus of research and attention on multi-sensor data fusion technology in the field of target tracking. Based on the above background, the research content of this paper is the application of multi-sensor target tracking fusion technology to achieve comprehensive warning information extraction of landslide multi-point monitoring data. This paper proposes a spatiotemporal registration method and Ensemble Kalman Filter (EnKF) for target tracking Algorithm, and the prediction model is established through optimization problem and target tracking. Finally, the test simulation is carried out by selecting 30 monitoring data of a monitoring point of a new construction project as the detection value. The experimental results show that when the moving target model is unknown in actual work, the polynomial. The order of the fitting and the boundary conditions of the spline fitting are not easy to give, but you can optionally use the sensor measurement data in the middle part and then use the spline fitting algorithm to obtain a better time registration effect; In terms of spatial registration, in the Z-axis direction, the measurement error of sensor A and sensor B is large, and the maximum value exceeds 2000 m. The maximum value of sensor A error between the measured values and theoretical values of radar A and B after registration is about 1000 m, which is the Z axis direction, and the maximum error of sensor B is about 1000 m, which is also the Z axis direction. The error has been significantly reduced. EnKF is better than the UKF algorithm under non-Gaussian noise conditions. The PF algorithm itself is suitable for nonlinear and non-Gaussian systems and has the best tracking performance.

In this study, we present a landslide susceptibility assessment carried out after the devastating 2008 Wenchuan earthquake. For the Zhouqu segment in the Bailongjiang basin in north-western China landslide susceptibility was computed by a logistic regression method. This region has been experiencing landslides for a long time, and numerous additional slope failures were triggered by the 2008 Wenchuan earthquake. The data used for this study consists of slope failures attributed to the 2008 earthquake, the 878 post Wenchuan earthquake landslides and collapses inventory build up by combination the field investigation, monoscopic manual interpretation, image classification and texture analysis using SPOT 5 and ALOS remote-sensing image data. All data derived from remote sensing images are validated during field investigations. The landslide pre-disposing factor database was constructed. A digital elevation model (DEM) with a 30 × 30 m resolution, orthophotos, geological and land-use maps and information on peak ground acceleration data from the 2008 earthquake is used. The statistical analysis of the relation between Wencuan earthquake-triggered landslides and pre-disposing factors show the great influence of lithological and topographical conditions for earthquake-triggered slope failures. The quality of susceptibility mapping was validated by splitting the study area into a training and validation set. The prediction capability analysis showed that the landslide susceptibility map could be used for land planning as well as emergency planning by local authorities in this region.

Landslide in alpine regions often causes heavy losses of both human lives and properties, most of the landslides are induced by heavy rainfall. In this paper, we put forward an early warning system of rain-induced landslide. From 2002, we carried on the demonstrative work of landslide monitoring and early warning in Yaan, Sichuan Province, China, and constructed the first county-scale landslide monitoring and early warning region. Yucheng District of Yaan City is located in the west of the Sichuan Basin, right in the intersection of SichuanBasin and the Tibetan Plateau. The slopes are made of Mesozoic sedimentary rock, sandstone inter-bedded with mudstone. Yucheng District has the title “sky funnel” because of the high precipitation, the annual precipitation is about 1750 mm. We carried out detailed landslide survey, and obtained the location, scale, characteristics, influence and triggering factors of the landslides. Then we assessed the regional landslide susceptibility. Based on the evolution law of the landslides, we selected ten factors to study the relationship between the factors and landslide. Using the bi-variate statistics method, we calculated the contribution to landslide from each factor, classified the susceptibility into four categories. We set up the regional rainfall monitoring network with 13 automatic CAWS600R rain gauges. Using the landslide survey data, we studied the rainfall influencing of the regional landslides. The one-day and three-day rainfall controls the occurrence of regional landslide. We also classified the triggering effect of rainfall into four categories. We presented a method to calculate the landslide danger degree using the susceptibility and triggering category. Utilizing the predicted rainfall data and real-time monitored rainfall data, together with the landslide susceptibility map, we developed a WebGIS-based landslide warning system, which greatly strengthened the capability for geohazard control.

Landslides have had a huge effect on human life, the environment and local economic development, and therefore they need to be well understood. In this study, we presented an approach for the analysis and modeling of landslide data using rare events logistic regression and applied the approach to an area in Lianyungang, China. Digital orthophotomaps, digital elevation models of the region, geological maps and different GIS layers including settlement, road net and rivers were collected and applied in the analysis. Landslides were identified by monoscopic manual interpretation and validated during the field investigation. To validate the quality of mapping, the data from the study area were divided into a training set and validation set. The result map showed that 4.26% of the study area was identified as having very high susceptibility to landslides, whereas the others were classified as having very low susceptibility (47.2%), low susceptibility (22.21%), medium susceptibility (14.39%) and high susceptibility (11.93%). The quality of the landslide-susceptibility map produced in this paper was validated, and it can be used for planning protective and mitigation measures. The landslide-susceptibility map is a fundamental part of the Lianyungang city landslide risk assessment.

A detailed landslide-susceptibility map was produced using a data-driven objective bivariate analysis method with datasets developed for a geographic information system (GIS). Known as one of the most landslide-prone areas in China, the Zhongxian-Shizhu Segment in the Three Gorges Reservoir region of China was selected as a suitable case because of the frequency and distribution of landslides. The site covered an area of 260.93 km 2 with a landslide area of 5.32 km 2 . Four data domains were used in this study, including remote sensing products, thematic maps, geological maps, and topographical maps, all with 25 m × 25 m pixels. Statistical relationships for landslide susceptibility were developed using landslide and landslide causative factor databases. All continuous variables were converted to categorical variables according to the percentile divisions of seed cells, and the corresponding class weight values were calculated and summed to create the susceptibility map. According to the map, 3.6% of the study area was identified as high-susceptibility. Extremely low-, very low-, low-, and medium-susceptibility zones covered 19.66%, 31.69%, 27.95%, and 17.1% of the area, respectively. The high- and medium-hazardous zones are along both sides of the Yangtze River, being in agreement with the actual distribution of landslides.

In China, the geological disasters of landslide and mud-rock flows cause losses of over 1000 lives and total economic losses of over 10 billions of RMB each year. There have been about 90,000 identified landslide sites, mostly distributed in several southern and northwestern provinces of China. In the reservoir area of the Three-Gorge project only, over 1000 landslide sides have been identified. A joint research was launched involving authors from a number of scientific institutions to explore technologies and methodologies for landslide monitoring with focus on the characteristics of the geological disasters at the up and middle reaches of Yangtze River. This paper studies the combined technologies for landslides monitoring and presents a demonstrative automatic landslide monitoring system in a chosen ancient landslide site, where the creeping movement process continues since its latest large sliding on August 25, 1981. The landslide test-bed is 500 m long and 300 m wide located in the Ya’an-Xiakou area in Sichuan province. To study the mechanism of the sliding process, 15 permanent GPS monuments were built in the area for regular observations. Automatic ombrometers, digital thermometers, underground water-level-meter and ground fissure-displacement-meter were set-up as well. The data from these automatic sensors are collected and automatically sent to the data process centre in Beijing via the Beidou-1 communication satellite. The paper also compares the landslide results from three GPS observing campaigns, demonstrates the feasibility to identify the displacements at the accuracy level of 2 mm using the dual-frequency GPS receivers. The results are encouraging and further analyses will be conducted, considering influences of non-GPS measurements.

In a case study of the Baota landslide in the impoundment area of the Three Gorges project, the conceptual model of groundwater systems in this large-scale landslide was established by integrating the data from hydraulics, physical–chemical characteristics, environmental isotopes, and temperatures of the groundwater in the landslide. The conceptual model of groundwater systems includes the recharge, seepage, discharge condition, and trend of groundwater which is in turn controlled by the environmental factors (e.g., rainfall, reservoir impoundment, and artificial surface water recharge). This is one of the keys to predict the slope stability and to apply countermeasures to mitigate landslides.

The paper describes the main types of geological hazard and associated risk in China. The engineering geological achievements and problems are also discussed together with the developing trends and strategies for controlling the geological hazards within the next 10 years. The paper summarises the mechanism of failure of mass movements in China as well as the various combinations of engineering geological and geotechnical measures for controlling different types of geological hazard. A good hazard monitoring system (which is made up of three types of monitoring method) and efficient administrative arrangements are stressed.

The population to be resettled from the Three Gorges Reservoir of the Yangtze River is about 1.2 million. During the first period of the Project from 1993 to 1997, about 82 thousand people were resettled; whereas about 550 thousand will be resettled during the second period of the Project from 1997 to 2003, and over 600 thousand will be resettled during the third period of the Project by 2009. Resettlement is a great challenge, since in the vicinity of the reservoir, there is not much land suitable for constructing buildings and the area is subject to various geological hazards. The paper discusses the engineering geology of the area, especially the geological hazards and their control for resettlement. The control measures for major geological hazards that have a possibility of disrupting the navigation through the Yangtze River (such as the Lianziya hazardous rock mass, the Huanglashi Landslide. and Jiguanling Rockfall) are discussed in this paper. The geological hazard prevention projects undertaken for the resettlement in the new sites (such as Waxian, Wushan, and Badong) is also described.