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Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models
นักวิจัย : Guo, Wanwu. , Xue, Heru.
คำค้น : Applied research. , 890205 Information Processing Services (incl. Data Entry and Capture). , 080108 Neural, Evolutionary and Fuzzy Computation. , 080110 Simulation and Modelling. , 080199 Artificial Intelligence and Image Processing not elsewhere classified. , Multilayer perceptron -- Nonlinear autoregressive neural network -- Nonlinear autoregressive with external input neural network -- Crop yield forecasting , Journal Article. Refereed, Scholarly Journal
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2557
อ้างอิง : http://hdl.cqu.edu.au/10018/1025151
ที่มา : Guo, W & Xue, H 2014, 'Crop yield forecasting using artificial neural networks: a comparison between spatial and temporal models', Mathematical Problems in Engineering, published online 23 January 2014, pp. 1-7, http://dx.doi.org/10.1155/2014/857865
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Mathematical problems in engineering. United States : Hindawi Publishing Corporation, 2014. Vol. 2014, (2014), p. 1-7 7 pages Refereed 1024-123X 1563-5147 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

บรรณานุกรม :
Guo, Wanwu. , Xue, Heru. . (2557). Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Guo, Wanwu. , Xue, Heru. . 2557. "Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Guo, Wanwu. , Xue, Heru. . "Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2557. Print.
Guo, Wanwu. , Xue, Heru. . Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models. กรุงเทพมหานคร : Central Queensland University, Australia; 2557.