Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions

文献类型： 外文期刊

第一作者： Wen, Xiaohu

作者： Wen, Xiaohu;Si, Jianhua;He, Zhibin;Yu, Haijiao;Wu, Jun;Shao, Hongbo;Shao, Hongbo

作者机构：

关键词： Support vector machine;Reference evapotranspiration modeling;Limited climatic data;Extreme arid regions

期刊名称：WATER RESOURCES MANAGEMENT （ 影响因子：3.517； 五年影响因子：3.868 ）

ISSN： 0920-4741

年卷期： 2015 年 29 卷 9 期

页码：

收录情况： SCI

摘要： Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET0) using limited climatic data. For the SVM, four combinations of maximum air temperature (T-max ), minimum air temperature (T-min ), wind speed (U-2 ) and daily solar radiation (R-s ) in the extremely arid region of Ejina basin, China, were used as inputs with T(max)and T-min as the base data set. The results of SVM models were evaluated by comparing the output with the ET0 calculated using Penman-Monteith FAO 56 equation (PMF-56). We found that the ET0 estimated using SVM with limited climatic data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological data. In particular, three climatic parameters, T-max , T-min , and R-s were enough to predict the daily ET0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET0 with scarce data in extreme arid regions.

分类号：

  • 相关文献

[1]Dynamic monitoring and driving power analysis of LUCC based on remote sensing in Beijing in recent thirty years. Gu, Xiaohe,Guo, Wei,Dong, Yansheng,Wang, Yanchang. 2013

[2]Survey of Support Vector Machine in the Processing of Remote Sensing Image. Li, Su,Wang, Wenchao. 2013

[3]Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Zhang Qing,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Cai Shu-hong,Yang Gui-jun. 2014

[4]A New Strategy in Observer Modeling for Greenhouse Cucumber Seedling Growth. Qiu, Quan,Qiao, Xiaojun,Zheng, Chenfei,Wang, Wenping,Yu, Jingquan,Shi, Kai,Bai, He. 2017

[5]Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Yuan, Huanhuan,Yang, Guijun,Wang, Yanjie,Liu, Jiangang,Yu, Haiyang,Feng, Haikuan,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong,Yuan, Huanhuan,Li, Changchun,Wang, Yanjie,Yuan, Huanhuan,Yang, Guijun,Liu, Jiangang,Feng, Haikuan,Yang, Xiaodong,Yang, Guijun,Yu, Haiyang,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong. 2017

[6]Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion. Yao, Sen,Liu, Hong-Gao,Li, Jie-Qing,Yao, Sen,Wang, Yuan-Zhong,Li, Tao,Wang, Yuan-Zhong. 2017

[7]Verification and predicting temperature and humidity in a solar greenhouse based on convex bidirectional extreme learning machine algorithm. Zou, Weidong,Yao, Fenxi,Zhang, Baihai,Guan, Zixiao,He, Chaoxing.

[8]Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features. Hasituya,Chen, Zhongxin,Wang, Limin,Wu, Wenbin,Li, He,Jiang, Zhiwei. 2016

[9]DIAGNOSTIC MODEL FOR WHEAT LEAF CONDITIONS USING IMAGE FEATURES AND A SUPPORT VECTOR MACHINE. Du, K.,Sun, Z.,Li, Y.,Zheng, F.,Chu, J.,Su, Y.. 2016

[10]Application of support vector machine for detecting rice diseases using shape and color texture features. Yao, Qing,Guan, Zexin,Zhou, Yingfeng,Tang, Jian,Hu, Yang,Yang, Baojun. 2009

[11]Feature Extraction and Classification of Animal Blood Spectra with Support Vector Machine. Lu Peng-fei,Fan Ya,Zhou Lin-hua,Gao Bin,Qian Jun,Liu Lin-na,Zhao Si-yan,Kong Zhi-feng. 2017

[12]Kharif Dryland Crop Identification Based on Synthetic Aperture Radar in the North China Plain. Dong Zhaoxia,Wang Di,Zhou Qingbo,Chen Zhongxin. 2015

[13]Quantitative Analysis of Near-Infrared Spectroscopy by Combined Stationary Wavelet Transform-Support Vector Machine. Xing, Li,Chen, Longjian. 2013

[14]The influence of soil particle sizes on hyperspectral prediction of soil organic matter content. Yao, Yanmin,Si, Haiqing,Wang, Deying,Huang, Qing,Chen, Zhongxin,Liu, Ying. 2015

[15]Geographical classification of apple based on hyperspectral imaging. Guo, Zhiming,Huang, Wenqian,Chen, Liping,Zhao, Chunjiang. 2013

[16]ESTIMATION OF MAIZE PLANTING AREA USING MIXED FIELD DECOMPOSITION OF MULTI-TEMPORAL TM IMAGES. Gu, Xiaohe,Dong, Yansheng,Ma, Li,Dong, Yingying. 2012

[17]Quantitative Sequence- Activity Model Analysis of Oligopeptides Coupling an Improved High- Dimension Feature Selection Method with Support Vector Regression. Dai, Zhijun,Zhang, Hongyan,Yuan, Zheming,Wang, Lifeng,Dai, Zhijun,Zhang, Hongyan,Bai, Lianyang,Yuan, Zheming,Bai, Lianyang. 2014

[18]STUDY ON AOTF-BASED NEAR-INFRARED SPECTROSCOPY ANALYSIS SYSTEM OF FARM PRODUCE QUALITY. Zhang, Xiaochao,Hu, Xiaoan,Zhang, Yinqiao,Wang, Hui,Zhang, Hui. 2009

作者其他论文 更多>>
  • Spatiotemporal variations of tenebrionid beetles (Coleoptera: Tenebrionidae) in the Gobi desert, Northwest China

    作者：Ren, Jialong;Xin, Weidong;Ren, Jialong;Zhao, Wenzhi;He, Zhibin;Wang, Yongzhen;Pan, Chengchen;Liu, Jiliang;Feng, Yilin;Niu, Yiping

    关键词：Gobi desert; precipitation change; tenebrionid beetles; temporal dynamics; spatial pattern

  • Soil macrofauna trophic structure and its relationship with soil factors in oases of contrasting cultivation ages

    作者：Wang, Yongzhen;Zhao, Wenzhi;He, Zhibin;Chen, Longfei;Liu, Jiliang;Wang, Yongzhen;Zhao, Wenzhi;He, Zhibin;Chen, Longfei;Liu, Jiliang;Ren, Jialong;Ren, Wei

    关键词：Abandoned cropland; Oasis farmland; Cultivated farmland; Trophic structure of soil macrofauna

  • Small G Protein OsRab6a Confers Cold Tolerance by Cooperating With OsRAN2 and Suppressing ABA Signalling in Rice

    作者：Li, Chenggang;Huang, Fenglin;Xie, Hongjun;Li, Chenggang;Huang, Fenglin;Xie, Hongjun;Li, Chenggang;Huang, Fenglin;Xie, Hongjun;Chen, Yue;Xiao, Yinghui;Jiang, Su;He, Huqiang;Huang, Shipeng;Li, Guangzhao;Chen, Chuanxin;Sun, Yuancheng;Wu, Jun;Wang, Dan;Chen, Rubin;Xiao, Yinghui;Jiang, Su;He, Huqiang;Huang, Shipeng;Li, Guangzhao;Chen, Chuanxin;Sun, Yuancheng;Wu, Jun;Wang, Dan;Chen, Rubin;Chen, Yue

    关键词：cold tolerance; Oryza Sativa. L; OsRab6a; OsRAN2; small-GTP protein

  • Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of 232 complex traits in pigs

    作者：Xu, Zhiting;Lin, Qing;Cai, Xiaodian;Zhong, Zhanming;Teng, Jinyan;Zeng, Haonan;Gao, Yahui;Wei, Chen;Zhou, Jun;Zhang, Wenjing;Zhang, Xiaoke;Shi, Shaolei;Wu, Jun;Diao, Shuqi;Liu, Yuqiang;Pan, Xiangchun;Feng, Xueyan;Liu, Ruiqi;Su, Zhanqin;Chang, Chengjie;Zhu, Qianghui;Wu, Yuwei;Zhang, Hao;Yuan, Xiaolong;Li, Jiaqi;Zhang, Zhe;Li, Bingjie;Gao, Yahui;Liu, George E.;Gao, Yahui;Ma, Li;Cai, Zexi;Bai, Zhonghao;Su, Guosheng;Sahana, Goutam;Lund, Mogens Sando;Fang, Lingzhao;Wang, Xiaoqing;Shi, Liangyu;Zhao, Fuping;Wang, Xue;Wang, Yi;Zhang, Zipeng;Ding, Xiangdong;Lin, Yu;Tang, Guoqing;Li, Mingzhou;Li, Xuewei;Liu, Shuli;Yin, Hongwei;Bai, Lijing;Li, Kui;Zhou, Zhongyin;Wang, Qishan;Pan, Yuchun;Xu, Zhong;Peng, Xianwen;Mei, Shuqi;Mo, Delin;Liu, Xiaohong;Chen, Yaosheng;Liu, Yang;Xiang, Ruidong;Xiang, Ruidong;Shen, Xia;Shen, Xia;Shen, Xia;Li, Pinghua;Huang, Ruihua;Li, Pinghua;Huang, Ruihua;Ballester, Maria;Crespo-Piazuelo, Daniel;Amills, Marcel;Clop, Alex;Amills, Marcel;Karlskov-Mortensen, Peter;Fredholm, Merete;Zhang, Qin;Zhao, Yunxiang

    关键词：pig; meta-GWAS; PigGTEx; molQTL; complex traits

  • Research note: Unveiling the impact of ovotransferrin on chicken primordial germ cells biological processes

    作者：Liu, Xin;Peng, Yixiu;Wu, Jun;Qian, Hongwu;Lv, Xiaoqian;Li, Fan;Jin, Kai;Niu, Yingjie;Chen, Guohong;Li, Bichun;Zuo, Qisheng;Liu, Xin;Peng, Yixiu;Wu, Jun;Qian, Hongwu;Lv, Xiaoqian;Li, Fan;Jin, Kai;Niu, Yingjie;Chen, Guohong;Li, Bichun;Zuo, Qisheng;Liu, Xin;Peng, Yixiu;Wu, Jun;Qian, Hongwu;Lv, Xiaoqian;Li, Fan;Jin, Kai;Niu, Yingjie;Chen, Guohong;Li, Bichun;Zuo, Qisheng;Song, Jiuzhou;Han, Wei;Li, Bichun

    关键词：Chicken; Primordial germ cell; ovotransferrin; chicken; serum

  • Characterization of a KANADI-like transcription factor that suppresses pear anthocyanin biosynthesis

    作者：Wei, Weilin;Chen, Guosong;Cao, Beibei;Qin, Mengfan;Wang, Runze;Li, Jiaming;Wu, Jun;Lin-Wang, Kui;Espley, Richard, V;Allan, Andrew C.;Sha, Shoufeng;Li, Juncai

    关键词：

  • Reducing methane emissions by developing lowfumarate high-ethanol eco-friendly rice

    作者：Jin, Yunkai;Hu, Jia;Liu, Chunlin;Wu, Jun;Jin, Yunkai;Hu, Jia;Liu, Chunlin;Wu, Jun;Jin, Yunkai;Hu, Jia;Bettembourg, Mathilde;Bedada, Girma;Sun, Chuanxin;Liu, Tong;Moazzami, Ali A.;Schnurer, Anna;Sun, Kai;Hao, Peiying;Tang, Jintian;Ye, Zihong;Yu, Xiaoping;Xue, Lihong;Li, Peng;Pan, Aihu;Weng, Lushui;Xiao, Guoying

    关键词：paddy; methane emission; root exudates; fumarate; ethanol; low-methane rice

微信小程序