作者机构：
Hunan Provincial Key Laboratory of Remote Sensing, Monitoring of Ecological Environment in Dongting Lake Area, Changsha, Hunan, China;School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China;[Zhen Xi; Yun Xue] Hunan Provincial Key Laboratory of Remote Sensing, Monitoring of Ecological Environment in Dongting Lake Area, Changsha, Hunan, China<&wdkj&>School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China
会议名称：
2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
会议时间：
14 July 2023
会议地点：
Guangzhou, China
会议论文集名称：
2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
关键词：
Water quality assessment;Support vector machine;Dongting Lake
摘要：
This paper presents 15 SVM models optimized by machine learning methods in water quality assessment. Through comparative analysis, we found that all the models reached reasonable accuracy during the training and testing process. In particular, SAA-SVM-PI, GWO-SVM-PI, GWO-SVM-TN, PSO-SVM-TP models have the highest accuracy, the highest squared correlation and the lowest mean squared error when testing the three indexes of PI, TN and TP. In order to study the spatial distribution characteristics of water quality in Dongting Lake, GWO-SVM-PI, GWO-SVM-TN and PSO-SVM-TP models were respectively used for single-factor water quality classification. The results showed that: As far as the PI is concerned, the water quality is mainly in grade I and II; As far as TN is concerned, the water quality is mainly of grade IV and V, with the worst water quality in the East Dongting Lake and the outlet profile; In terms of TP, the water quality is mainly of grade III, IV and V, with the best inlet water quality, the worst water quality in the South Dongting Lake.
作者机构：
[Weizhen Ji] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China;Hunan Provincial Key Laboratory of Remote Sensing, Monitoring of Ecological Environment in Dongting Lake Area, Changsha, Hunan, China;Hunan Provincial Natural Resources Affairs Center, Changsha, Hunan, China;[Zhong-man Duan] Hunan Provincial Key Laboratory of Remote Sensing, Monitoring of Ecological Environment in Dongting Lake Area, Changsha, Hunan, China<&wdkj&>Hunan Provincial Natural Resources Affairs Center, Changsha, Hunan, China;[Yun Xue] Hunan Provincial Key Laboratory of Remote Sensing, Monitoring of Ecological Environment in Dongting Lake Area, Changsha, Hunan, China<&wdkj&>School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China
会议名称：
2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
会议时间：
14 July 2023
会议地点：
Guangzhou, China
会议论文集名称：
2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
关键词：
Water quality assessment;Support vector machine;Gray wolf optimization algorithm;Dongting Lake
摘要：
Water quality assessment is an essential part of water resource management. This paper presents a new GWO-SVM method for water quality assessment. Through comparison with GS-SVM and GA-SVM, we found that GWO-SVM model has the highest accuracy, the highest squared correlation and the lowest mean squared error when testing the three indexes of PI, TN and TP.The GWO-SVM model proposed in this paper was used to analyze the water quality data of Dongting Lake from 1993 to 2012. The results showed that: As far As the PI is concerned, the water quality is mainly in grade I and II, with the best water quality in autumn and 2008-2012.As far As TN is concerned, the water quality is mainly of grade IV and V,With the best water quality in autumn and the worst in spring, water quality was the best from 2003 to 2007 and the worst from 2008 to 2012.In terms of TP, the water quality is mainly of grade III,IV and V, with the best inlet water quality, the worst water quality In the south dongting lake, the best water quality In summer,The best water quality between 1993 and 1997, and the worst water quality between 2003 and 2007.
作者机构：
[Ji, Weizhen; Liu, Deer] School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou;341000, China;[Meng, Yifei] School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan;430074, China;[Xue, Yun] School of Municipal and Surveying Engineering, Hunan City University, Yiyang
通讯机构：
[Deer Liu] S;School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, China
作者机构：
[Liu C.; Wen Y.] Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China;[Xue Y.] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, China
会议名称：
27th International Conference on Neural Information Processing, ICONIP 2020
作者机构：
[Wen Y.; Feng C.; Zhou Q.] Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China;[Xue Y.] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, China
会议名称：
27th International Conference on Neural Information Processing, ICONIP 2020
作者机构：
[陈宇波] College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing;210023, China;[薛云; 邹滨] Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-physics, Central South University, Changsha;410083, China;[周松林] School of Municipal and Surveying Engineering, Planning, Architectural Design and Research Institute, Hunan City University, Yiyang
通讯机构：
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-physics, Central South University, Changsha, China
作者机构：
School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, China;School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China;School of Municipal and Surveying Engineering, Hunan City University, Yiyang, China;Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha, China
摘要：
Parameters optimization is a research hotspot of SVM and has gained increasing interest from various research fields. Compared with other optimization algorithms, genetic-based evolutionary algorithms that have achieved optimization according to the laws of separation and free combination in genetics are gradually attracted much attention. Also, due to the characteristics of self-organization and self-adaptation, these algorithms often enable SVM to obtain appropriate parameters, so that the model can be applied to more applications. Additionally, many improvements have been proposed in the past two decades in order to allow the optimized SVM model to obtain better performance. This work focuses on reviewing the current state of genetic-based evolutionary algorithms used to optimize parameters of SVM and its variants. First, we introduce the principles of SVM and provide a survey on optimization methods of its parameters. Then we propose a taxonomy of improving genetic-based evolutionary algorithms according to code mechanism, parameters control, population structure, evolutionary strategy, operation mechanism, operators, and many other hybrid approaches. Furthermore, this paper analyzes and compares the advantages and disadvantages of the above algorithms explicitly, and provides their applicable scenarios as well. Finally, we highlight the existing problems of genetic-based evolutionary algorithms used for parameters optimization of SVM and prospect development trends of this field in the future.
期刊：
IOP Conference Series: Earth and Environmental Science,2019年330(2):022099 ISSN：1755-1307
作者机构：
[Yang Y.] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China;Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha, Hunan, China;[Liu J.] School of Tourism, Central South University of Forestory and Technology, Changsha, China;[Sun Y.] School of Science, Central South University of Forestory and Technology, Changsha, China;[Xue Y.] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China<&wdkj&>Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha, Hunan, China
会议名称：
2019 International Conference on Advances in Civil Engineering, Energy Resources and Environment Engineering, ACCESE 2019
作者：
Yun Xue;Fanghua Tang;Shishi Liu;Jianglong Liu;Yurong Sun
期刊：
IOP Conference Series: Earth and Environmental Science,2019年349(1):012014 ISSN：1755-1307
作者机构：
[Tang F.; Liu S.] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China;Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha, Hunan, China;[Liu J.] School of Tourism, Central South University of Forestry AndTechnology, Changsha, China;[Sun Y.] School of Science, Central South University of Forestry AndTechnology, Changsha, China;[Xue Y.] School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan, China<&wdkj&>Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha, Hunan, China
会议名称：
2nd International Workshop on Environment and Geoscience, IWEG 2019
会议时间：
17 July 2019 through 19 July 2019
关键词：
Forestry;Geology;Spatial variables measurement;Time series analysis;Construction land;Cultivated lands;Hunan province , China;Land-use change;Regional differences;Relative rates;Spatial variations;Transfer rates;Land use
会议名称：
International Joint Conference on Neural Networks (IJCNN)
会议时间：
JUL 06-11, 2014
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Shi, Zhongwei] Guilin Univ Elect Technol, Sch Comp Sci & Engn, Guilin, Peoples R China.^[Wen, Yimin] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.^[Xue, Yun] Hunan City Univ, Sch Municipal & Surveying Engn, Changsha, Hunan, Peoples R China.^[Cai, Guoyong] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.
会议论文集名称：
IEEE International Joint Conference on Neural Networks (IJCNN)
关键词：
class incremental learning;concept drift;evolving data streams;multi-label classification
摘要：
Multi-label stream classification has not been fully explored for the unique properties of large data volumes, realtime, label dependencies, etc. Some methods try to take into account label dependencies, but they only focus on the existing frequent label combinations, leading to worse performance for multi-label classification. To deal with these problems, this paper proposes an algorithm which dynamically recognizes some new frequent label combinations and updates the trained classifier by class incremental learning strategy. Experimental results over both real-world and synthetic datasets demonstrate its better predictive performance.
