Uzun Kısa Süre Bellek (LSTM) ile Toprak Sıcaklığının Tahmini

Abstract

Soil temperature not only affects many soil properties, but also has a significant effect on plant development. Knowing and correct estimation of soil temperature is important for both soil management and crop production. The accuracy of temperature forecasts is very important, especially for the countries that stand out with their agriculture-based economies. Therefore, in recent years, different artificial intelligence methods have been used in soil temperature predictions. Deep learning methods lead the way in achieving high prediction accuracy. In this study, a Long Short-Term Memory (LSTM) network, which is a deep learning (DL) sub-architecture, is proposed to create an effective model for soil temperature prediction. The data used in the study are the daily soil temperatures at a depth of 50 cm for the years 2013-2021 of Bingöl province. For the training of the proposed LSTM model, 89% of the data set within the scope of the study was used, and. The remaining 11% was estimated by the model for assessing model success. The RMSE value as a result of the estimation made by the trained LSTM model was obtained as 1,25. The high estimation accuracy of the proposed model showed that this model could be successfully applied in temperature data estimation studies.