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An incorporative statistic and neural approach for crop yield modelling and forecasting

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : An incorporative statistic and neural approach for crop yield modelling and forecasting
นักวิจัย : Guo, Wanwu. , Xue, Heru.
คำค้น : Crop yields Forecasting. , Applied research. , 890202 Application Tools and System Utilities. , 890205 Information Processing Services (incl. Data Entry and Capture) , 080105 Expert Systems. , 080108 Neural, Evolutionary and Fuzzy Computation. , 080110 Simulation and Modelling. , Crops and climate. , Crop yields Simulation methods. , Neural networks -- Correlation analysis -- Crop yield -- Modelling and forecasting , Journal Article. Refereed, Scholarly Journal
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2555
อ้างอิง : http://hdl.cqu.edu.au/10018/928347
ที่มา : Guo, WW & Xue H 2012, An incorporative statistic and neural approach for crop yield modelling and forecasting, Neural Computing and Applications, vol. 21, no.1, pp. 109-117, http://dx.doi.org/10.1007/s00521-011-0636-0
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Neural computing and applications. United Kingdom : Springer, 2012. Vol. 21, no.1 (2012), p. 109-117 9 pages Refereed 0941-0643 1433-3058 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

An incorporative framework is proposed in this study for crop yield modelling and forecasting. It is a complementary approach to traditional time series analysis on modelling and forecasting by treating crop yield and associated factors as a non-temporal collection. Statistics are used to identify the highly related factor(s) among many associates to crop yield, and then play a key role in data cleaning and a supporting role in data expansion, if necessary, for neural network training and testing. Wheat yield and associated plantation area, rainfall, and temperature in Queensland of Australia over 100 years are used to test this incorporative approach. The results show that well-trained multilayer perceptron models can simulate the wheat production through given plantation areas with a mean absolute error (MAE) of ~2%, whereas the third-order polynomial correlation returns an MAE of ~20%. However, statistical analysis plays a key role in identifying the most related factor, detecting outliers, determining the general trend of wheat yield with respect to plantation area, and supporting data expansion for neural network training and testing. The combination of these two methods provides both meaningful qualitative and accurate quantitative data analysis and forecasting. This incorporative approach can also be useful in data modelling and forecasting in other applications due to its generic nature.

บรรณานุกรม :
Guo, Wanwu. , Xue, Heru. . (2555). An incorporative statistic and neural approach for crop yield modelling and forecasting.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Guo, Wanwu. , Xue, Heru. . 2555. "An incorporative statistic and neural approach for crop yield modelling and forecasting".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Guo, Wanwu. , Xue, Heru. . "An incorporative statistic and neural approach for crop yield modelling and forecasting."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2555. Print.
Guo, Wanwu. , Xue, Heru. . An incorporative statistic and neural approach for crop yield modelling and forecasting. กรุงเทพมหานคร : Central Queensland University, Australia; 2555.