计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 496-503.doi: 10.11896/jsjkx.200200059

• 大数据&数据科学 • 上一篇    下一篇

基于多种模型的云南省农作物主产区域部分气候指标分析与预测

陈沛, 郑万波, 刘文奇, 肖敏, 张凌霄   

  1. 1 昆明理工大学理学院 昆明 650500
    2 昆明理工大学数据科学研究中心 昆明 650500
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 郑万波(zwanbo2001@163.com)
  • 作者简介:1262955357@qq.com
  • 基金资助:
    国家自然科学基金项目(61472051);重庆市社会事业与民生保障科技创新专项(cstc2016shmszx90002);昆明理工大学人培项目(KKZ3201907003)

Analysis and Forecast of Some Climate Indexes in Main Producing Areas of Yunnan Province Based on Multiple Models

CHEN Pei, ZHENG Wan-bo, LIU Wen-qi, XIAO Min, ZHANG Ling-xiao   

  1. 1 School of Science,Kunming University of Science and Technology,Kunming 650500,China
    2 Data Science Research Center,Kunming University of Science and Technology,Kunming 650500,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:CHEN Pei,born in 2000,postgraduate.His main research interests include application of data science and big data technology in intelligent industry.
    ZHENG Wan-bo,born in 1981,Ph.D,associate researcher.His main research interests include big data intersects with cloud computing,data analysis and mining,mine tunnel emergency management,as well as research on mine tunnel emergency rescue and disposal information technology and equipment,engineering geophysical exploration,measurement and control technology and instruments.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61472051),Chongqing Social Undertakings and Livelihood Security Science and Technology Innovation Project Special Program(cstc2016shmszx90002) and Kunming University of Science and Technology Talent Training Project Fund(KKZ3201907003).

摘要: 针对目前云南省缺乏农作物种植与气候指标预测模型与建模方法的问题,首先,概述了降水量、温度、空气湿度等主要气候因素的数据分析预测模型的研究现状,分析温度、降雨量、湿度与农业气候资源的综合关系,进行数据清洗,并筛选出主要分析指标;其次,使用1981年到2010年共30年的数据分析云南省的降水量、温度、空气湿度模型;再次,采用Matlab Curve Fitting Tool拟合函数进行气候预测,得到了所选取地区的气候指标的预测模型并计算预测误差,进行数值拟合误差分析;最后,利用SPSS软件建立ARIMA模型,以此作为前述模型的补充修正。通过实验验证,成功地将模型90%的预测值误差控制在10%以内。本研究建立了针对云南省主产区域部分气候指标分析与预测模型,对云南农作物种植区域规划有一定指导作用。

关键词: ARIMA模型, MATLAB应用, 环境监测, 气候模型拟合, 区域气候预测, 数据挖掘与分析

Abstract: In view of the lack of prediction models and modeling methods of crop planting and climate index in Yunnan Province,firstly,the research status of data analysis and prediction models of main climatic factors such as precipitation,temperature and air humidity are summarized.The comprehensive relationship between temperature,rainfall,humidity and agroclimatic resources are analyzed,the data are cleaned,and the main analysis indexes are selected.Secondly,the precipitation,temperature and air humidity model of Yunnan Province are analyzed by using the data of 30 years from 1981 to 2010.Thirdly,the Matlab Curve Fitting Tool fitting function is used to predict the climate,and the prediction model of the climate index in the selected area is obtained and the prediction error is calculated,and the numerical fitting error analysis is carried out.Finally,the ARIMA model is established by SPSS software,which is used as a supplement to the above-mentioned model.Through experimental verification,the prediction error of model 90% is successfully controlled within 10%.Through this study,a model for analyzing and predicting some climate indexes in the main producing areas of Yunnan Province is established,which plays a guiding role in the regional planning of crop planting in Yunnan Province.

Key words: ARIMA model, Climate model fitting, Data mining and analysis, Environmental monitoring, MATLAB application, Regional climate prediction

中图分类号: 

  • TP391.9
[1] LIU D.An Empirical Study on the Effect of Industrial Structure Optimization on Economic Growth in Yunnan Province [D].Kunming:Yunnan University of Finance and Economics,2018.
[2] LIN YU,LI X L,JIANG H Q,et al.Discussion on Comparative Advantage and Distribution of Main Crop Production in Yunnan Province [J].Exploration of Economic problems,2005(5):142-144.
[3] ZHANG C X.Study on Climate Suitability of Main CroppingSystems in South China under the Background of Climate Change [D].Nanchang:Jiangxi Agricultural University,2016.
[4] HUANG X F,SHAO D G,YANG S M.A Decomposition Prediction Model for Rainfall Time Series and Its Application [J].China Rural Water Conservancy and Hydropower,2007(9):6-8.
[5] HUANG X F,SHAO D G,YANG S M.Application of OrdinalSet Pair Analysis in Annual Rainfall Prediction of Liao River Basin [J].China Rural Water Conservancy and Hydropower,2007(9):6-8.
[6] ZHAO G C,ZHAO P F,WANG G Y.Study on Temporal and Spatial Distribution Characteristics and Prediction Model of Rainfall in Tianjin City[J].Haihe Water Conservancy,2019(4):41-43.
[7] CHEN C,XU F X,PANG Y M,et al.Prediction of thresholdtemperature start date for rice at critical development stages in Southwest China [J].Chinese Journal of Ecological Agriculture (in English and Chinese),2019,27(8):1172.
[8] HE Q Q,GUO X H,LEI T,et al.Prediction of WinterSoil Temperature of Apple Orchard Under Water Storage Pit Irrigation Based on improved BP Neural Networks [J].Water-saving Irrigation,2019(7):16-20.
[9] XU T Y,WANG T K,ZHANG X B,et al.Application of RBF Neural Network in Humidity Simulation and Prediction of Northern Sunlight Greenhouse[J].Journal of Shenyang Agricultural University,2014,45(6):726-730.
[10] YAN M L.Characteristic Analysis and Prediction of Farmland Microclimate Time Series Data [D].Tai'an:Shandong Agricultural University,2018.
[11] BAI Z W,ZHANG L,WANG J,et al.Study on meteorological drought prediction in Yunnan Province based on ARIMA[J].People's Yangtze River,2015,46 (15):6.
[12] DENG X Z,ZHAO C H,YUAN Y W.Analysis of the drought risk in Yunnan Province Based on the WRF model[J].Population,Resources and Environment of China,2013,23(10):95-101.
[13] LU X J,XIE H P,MU S L.Study on drought Climate Prediction of Yunnan Province based on Grey catastrophe Theory[J].Mathematics In Practice And Theory.2015,45(1):164-168.
[14] JI W J,ZHANG M S,HU X Q.Characteristics of agroclimatic resources and their effects on agricultural production in Yunnan in 2016 [J].Yunnan Agricultural Science and Technology,2017(5):8-11.
[15] LI Z C,LIU S.Prediction comparison based on ARIMA model,grey model and regression model [J].Statistics and decision-making,2019,35(23):38-41.
[16] ZHANG T,PAN E S.Degradation Modeling of CapacitorsBased on Time Series Analysis [J].Journal of Shanghai Jiaotong University,2019,53(11):1316-1325.
[17] ZHANG X W,GUO Z Y,CHEN L H.Brake temperature prediction method based on ARIMA model [J/OL].Journal of Jilin University (Engineering Edition).[2020-01-19].https://doi.org/10.13229/j.cnki.jdxbgxb20190656.
[18] WANG D G,HUANG L,CHANG J,et al.Load forecastingmodel based on ARIMA and CART [J].Journal of Shenzhen University (Science and Technology Edition),2019,36(3):245-251.
[19] WANG D.GIS-Based Rrgional Climatic Suitability for Potato Planting in Yunnan Province [D].Kunming:Yunnan Agricul-tural University,2017.
[20] ZHU Y,LI S Y,ZHANG S B.Climate Resource and Adaptability of Rice and Winter-Sown Maize in Southern Subtropical Zone of Yunnan[J].Journal of Nanjing Institute of Meteorology,2000,23(2):299404.
[21] ZHAO F,QIAN H S.The research advances on the crop climate suitability influenced by global warming [J].Chinese Journal of Ecological Agriculture,2004,12(12):134-137.
[22] XU S T,HE X,YANG P W,et al.Effect of Climatic Factors on the Banana Growth and ItsFruit Quality at Different Altitudes [J/OL].Journal of Yunnan Agricultural University (Natural Science).[2020-01-17].http://kns.cnki.net/kcms/detail/53.1044.S.20200115.0855.001.html.
[23] ZHAO P,LIN K H.Effect of Potassium Fertilizer on Quality of Crops [J].Journal of Yunnan Agricultural University,2001,16(1):56-59.
[1] 李晓东, 於志勇, 黄昉菀, 朱伟平, 涂淳钰, 郑伟楠.
面向河道环境监测的群智感知参与者选择策略
Participant Selection Strategies Based on Crowd Sensing for River Environmental Monitoring
计算机科学, 2022, 49(5): 371-379. https://doi.org/10.11896/jsjkx.210200005
[2] 李双刚, 张爽, 王兴伟.
基于自适应虚拟机迁移的云资源调度机制
Cloud Resource Scheduling Mechanism Based on Adaptive Virtual Machine Migration
计算机科学, 2020, 47(9): 238-245. https://doi.org/10.11896/jsjkx.190900189
[3] 徐鹤, 吴满星, 李鹏.
基于ARIMA模型的RFID室内相对位置定位算法
RFID Indoor Relative Position Positioning Algorithm Based on ARIMA Model
计算机科学, 2020, 47(9): 252-257. https://doi.org/10.11896/jsjkx.200400038
[4] 王栋, 王虎, 姜迁里.
基于6LoWPAN的低功耗长距离海洋环境监测系统
Low Power Long Distance Marine Environment Monitoring System Based on 6LoWPAN
计算机科学, 2020, 47(6A): 596-598. https://doi.org/10.11896/JsJkx.190900194
[5] 周万锴, 龙敏.
基于区块链的环境监测数据安全传输方案
Secure Transmission Scheme for Environmental Monitoring Data Based on Blockchain
计算机科学, 2020, 47(1): 315-320. https://doi.org/10.11896/jsjkx.190100195
[6] 王栋, 袁伟, 吴迪.
基于WiFi物联网的图书馆环境监测系统
Monitoring System for Library Environment Based on WiFi Internet of Things
计算机科学, 2018, 45(11A): 532-534.
[7] 赵美惠.
面向环境监测的无线传感器网络的数据流挖掘研究
Study on Mining Data Streams in WSNs for Environment Monitoring
计算机科学, 2012, 39(Z11): 111-113.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!