Ji Yu PPRHe SVMMo Xing Yan Jiu Dong Ting Hu Jing Liu Yu Shu Sha Liang Bian Hua Study on Runoff and Sediment Variation of the Dongting Lake Based on PPR and SVM Model
Dong Ting Hu Shi Chang Jiang Zhong You Dian Xing De Tun Tu Xing Diao Xu Hu Bo Shou Zi Ran He Ren Wei Yin Su Ying Xiang ,Qi Shu Ru He Shu Chu De Jing Liu Yu Shu Sha Liang Guan Xi Xian Hou Fa Sheng Liao Duo Ci Da De Diao Zheng Guo Cheng Wei Liao Tan Ming Dong Ting Hu Shui Sha Chu Ru Hu Liang Bian Hua He Xiang Hu Guan Xi ,Ben Wen Li Yong Xian You Shui Wen Ni Sha Deng Guan Ce Shi Yan Zi Liao ,Ji Yu Tou Ying Xun Zong Hui Gui (PPR)He Zhi Chi Xiang Liang Ji Mo Xing (SVM)Dui Dong Ting Hu Jing Liu Yu Shu Sha Liang Bian Hua Te Zheng Jin Xing Liao Mo Ni He Yan Zheng ,Bing Dui Mo Ni Wu Chai Jin Xing Liao Dui Bi Jie Guo Biao Ming ,Liang Chong Mo Xing Jun Ke Yi Yong Yu Mo Ni Dong Ting Hu Shui Wang Jing Liu Yu Shu Sha Liang Guan Xi ,Dan Zhi Chi Xiang Liang Ji Mo Xing (SVM)Jing Que Du Jiao Gao ,Gua Yong Xing Jiao Hao ,Ke Wei Dong Ting Hu De Zong He Zheng Zhi Ti Gong Li Lun Zhi Cheng He Ke Xue Yi Ju The Dongting Lake is very important for flood storage and water sources in the midstream of the Yangtze River. Due to the dual effects of nature and human activities, several major changes have been taken place on the runoff and sediment conditions and the relationships between rivers and lakes successively. In order to explore the relationships of runoff and sediment in and out the lake, the existing hydrologic and sediment and other observation data of the Dongting Lake area are fully used, and two non linear simulation models, Projection Pursuit Regression (PPR) and Support Vector Machine (SVM) are established. The simulation errors are also compared. The results show that the Support Vector Machine (SVM) has better validity and credibility, which could be used as an effective tool to simulate the complicated river system. This finding provides theoretical support and scientific basis for comprehensive improvement and ecological restoration of the Dongting Lake.