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首页> 外文期刊>Turkish Journal of Agriculture & Forestry >The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey
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The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey

机译：使用人工神经网络预测土耳其阿达纳的月平均土壤温度

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摘要

The objective of this paper was to develop an artificial neural network (ANN) model in order to predict monthly mean soil temperature for the present month by using various previous monthly mean meteorological variables. For this purpose, the measured soil temperature and other meteorological data between the years of 2000 and 2007 at Adana meteorological station were used. The soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the ground level by the Turkish State Meteorological Service (TSMS). A 3-layer feed-forward artificial neural network structure was constructed and a back-propagation algorithm was used for the training of ANNs. The models consisting of the combination of the input variables were constructed and the best fit input structure was investigated. The performances of ANN models in training and testing procedures were compared with the measured soil temperature values to identify the best fit forecasting model. The results show that the ANN approach is a reliable model for prediction of monthly mean soil temperature. Key words: Artificial neural network, meteorological variables, prediction, soil temperature
机译：本文的目的是开发一个人工神经网络（ANN）模型，以便通过使用各种以前的每月平均气象变量来预测本月的每月平均土壤温度。为此，使用了2000年至2007年Adana气象站的测得土壤温度和其他气象数据。土耳其国家气象局（TSMS）在低于地面5、10、20、50和100厘米的深度测量了土壤温度。构造了三层前馈人工神经网络结构，并将反向传播算法用于人工神经网络的训练。构建了由输入变量组合组成的模型，并研究了最佳拟合输入结构。将ANN模型在训练和测试程序中的性能与测得的土壤温度值进行比较，以确定最佳拟合预测模型。结果表明，人工神经网络方法是预测月平均土壤温度的可靠模型。关键词：人工神经网络，气象变量，预测，土壤温度

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