Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks
Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks
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使用人工神经网络进行能量流建模并预测伊朗水稻品种的产量
DOI：
10.1016/j.energy.2017.06.089
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Nikkhah
中科院分区：
文献类型：
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作者：
Alireza Taheri;M. Khojastehpour;A. Rohani;S. Khoramdel;A. Nikkhah
利用Cobb-Douglas（CD）模型和人工神经网络（ANN）对伊朗戈莱斯坦省不同水稻品种的生产能流进行模拟。研究的能源投入有人力、农业机械、柴油、种子、杀虫剂、化肥、农家肥和电力。结果表明，高产长粒水稻（H-YLG）和优质长粒水稻（H-GLG）生产的总能耗分别为108，392和89，835兆焦耳/公顷。CD模型和能量投入对水稻产量的敏感性分析表明，人工、农药和化肥投入对水稻产量的影响大于其他投入。人工神经网络的结果表明，最好的模型包括一个输入层的8个输入，一个隐藏层的25个神经元和输出层（8-25-1拓扑结构）的H-YLG和H-GLG水稻品种。对CD模型和ANN模型预测H-GLG稻谷产量的比较研究表明，ANN模型的决定系数（R2）为0.99，均方根误差（RMSE）为1.93 kga ~（-1），比CD模型的预测精度高。此外，人工神经网络模型预测H-YLG产量比CD模型更准确，精度更高。
The aim of this study was to model the energy flows for the production of different paddy rice cultivars in Golestan province of Iran using Cobb-Douglas (CD) model and artificial neural network (ANN). The studied energy inputs were human labor, agricultural machinery, diesel fuel, seed, biocide, chemical fertilizers, farmyard manure and electricity. The results indicated that the total energy consumption of paddy farms for high-yielding long grain (H-YLG) and high-grade long grain (H-GLG) paddy production were 108,392 and 89,835 MJha−1, respectively. Also, CD model and sensitivity analysis of energy inputs on paddy yield showed that the impacts of human labor, biocide and chemical fertilizers inputs on paddy yield were higher than the other inputs. The ANN results highlighted that the best model consisted of an input layer with eight inputs, one hidden layer with 25 neurons and an output layer (8–25–1 topology) for both H-YLG and H-GLG paddy cultivars. The results obtained from the comparative study of CD and ANN models for predicting the amount of H-GLG paddy production revealed that the ANN model with the determination coefficient (R2) of 0.99 and the root mean square error (RMSE) of 1.93 kgha1estimated the outputs more accurately than the CD model. Besides, the ANN model estimated more accurate and precise outputs than CD model for predicting the production of H-YLG.