scholarly journals Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China

2019 ◽  
Vol 138 (1-2) ◽  
pp. 1095-1107 ◽  
Author(s):  
Ji-Long Chen ◽  
Hong Yang ◽  
Ming-Quan Lv ◽  
Zuo-Lin Xiao ◽  
Sheng Jun Wu
Keyword(s):  
Support Vector Machine ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Three Gorges ◽  
Pan Evaporation ◽  
Reservoir Area

scholarly journals Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yankun Wang ◽  
Huiming Tang ◽  
Tao Wen ◽  
Junwei Ma ◽  
Zongxing Zou ◽  
...  
Keyword(s):  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Three Gorges ◽  
Noise Variance ◽  
Displacement Prediction ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Landslide Displacement Prediction

Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement.

Time Series Prediction of Landslide Displacement Using SVM Model: Application to Baishuihe Landslide in Three Gorges Reservoir Area, China

2012 ◽  
Vol 239-240 ◽  
pp. 1413-1420 ◽  
Author(s):  
Chuan Hua Zhu ◽  
Guang Dao Hu
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Three Gorges Reservoir ◽  
Time Series Prediction ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Landslide Monitoring ◽  
Three Gorges ◽  
Reservoir Area ◽  
Baishuihe Landslide

Time series analysis has the ability to forecast the evolve trend of complex systems, which has been the issue in the research of landslide displacement dynamic forecasting. The Support Vector Machine (SVM) regression, we proposed, has been applied in Baishuihe landslide in Three Gorges Reservoir Area, China. The Oracle Data Mining (ODM) PL / SQL API have been introduced to build the SVM regression model for data mining process. The data was divided into two parts, wherein the first 36 months data used for training, and the other 6 months data used for validation. The results show that the error rate of the previous 5 was controlled within 8% and the accuracy of the 6th is 84.1%, which indicates SVM regression is reliable to calculate the displacement factors and can be used in short term prediction of landslide monitoring data.

scholarly journals Evaluation of ESV Change under Urban Expansion Based on Ecological Sensitivity: A Case Study of Three Gorges Reservoir Area in China

2021 ◽  
Vol 13 (15) ◽  
pp. 8490
Author(s):  
Hongjie Peng ◽  
Lei Hua ◽  
Xuesong Zhang ◽  
Xuying Yuan ◽  
Jianhao Li
Keyword(s):  
Three Gorges Reservoir ◽  
Urban Expansion ◽  
Urban Land ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Ecological Sensitivity ◽  
Reservoir Area ◽  
Green Development ◽  
Source Data

In recent years, ecosystem service values (ESV) have attracted much attention. However, studies that use ecological sensitivity methods as a basis for predicting future urban expansion and thus analyzing spatial-temporal change of ESV are scarce in the region. In this study, we used the CA-Markov model to predict the 2030 urban expansion under ecological sensitivity in the Three Gorges reservoir area based on multi-source data, estimations of ESV from 2000 to 2018 and predictions of ESV losses from 2018 to 2030. Research results: (i) In the concept of green development, the ecological sensitive zone has been identified in Three Gorges reservoir area; it accounts for about 35.86% of the study area. (ii) It is predicted that the 2030 urban land will reach 211,412.51 ha by overlaying the ecological sensitive zone. (iii) The total ESV of Three Gorges Reservoir area showed an increasing trend from 2000 to 2018 with growth values of about USD 3644.26 million, but the ESVs of 16 districts were decreasing, with Dadukou and Jiangbei having the highest reductions. (iv) New urban land increases by 80,026.02 ha from 2018 to 2030. The overall ESV losses are about USD 268.75 million. Jiulongpo, Banan and Shapingba had the highest ESV losses.

scholarly journals Effects of Landscape Positions and Landscape Types on Soil Properties and Chlorophyll Content of Citrus in a Sloping Orchard in the Three Gorges Reservoir Area, China

2021 ◽  
Vol 13 (8) ◽  
pp. 4288
Author(s):  
Siyue Sun ◽  
Guolin Zhang ◽  
Tieguang He ◽  
Shufang Song ◽  
Xingbiao Chu
Keyword(s):  
Soil Properties ◽  
Chlorophyll Content ◽  
Three Gorges Reservoir ◽  
Slope Position ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Landscape Types

In recent years, soil degradation and decreasing orchard productivity in the sloping orchards of the Three Gorges Reservoir Area of China have received considerable attention both inside and outside the country. More studies pay attention to the effects of topography on soil property changes, but less research is conducted from the landscape. Therefore, understanding the effects of landscape positions and landscape types on soil properties and chlorophyll content of citrus in a sloping orchard is of great significance in this area. Our results showed that landscape positions and types had a significant effect on the soil properties and chlorophyll content of citrus. The lowest soil nutrient content was detected in the upper slope position and sloping land, while the highest exists at the footslope and terraces. The chlorophyll content of citrus in the middle and upper landscape position was significantly higher than the footslope. The redundancy analysis showed that the first two ordination axes together accounted for 81.32% of the total variation, which could be explained by the changes of soil total nitrogen, total phosphorus, total potassium, available nitrogen, available potassium, organic matter, pH, and chlorophyll content of the citrus. Overall, this study indicates the significant influence of landscape positions and types on soil properties and chlorophyll content of citrus. Further, this study provides a reference for the determination of targeted land management measures and orchard landscape design so that the soil quality and orchard yield can be improved, and finally, the sustainable development of agriculture and ecology can be realized.

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