Maximum Temperature Forecasting with an Automatic Forecasting Method Based on Deep Dendritic Artificial Neural Network (AutoDeepdenT)
DOI:
https://doi.org/10.37134/jsml.vol14.1.10.2026Keywords:
Forecasting, Deep Artificial Neural Network, Dendritic Neuron Model, Temperature Prediction, Explainable AIAbstract
Due to the global ecological crisis, accurate temperature prediction has become increasingly important, especially for environmental sustainability worldwide. The main motivation for this research is the increasing importance of temperature prediction due to the global ecological crisis. Considering the impacts of climate change and environmental sustainability, making accurate temperature predictions has become a critical necessity for the conservation of natural resources and the fight against climate change. In addition to traditional statistical techniques, the success of deep learning methods in solving complex relationships has become the focal point of research in this field. A large number of statistical techniques are used to predict air temperatures, but deep learning methods have recently become popular for complex relationships. More layers distinguish Deep Artificial Neural Networks (DANNs) from traditional Artificial Neural Networks (ANNs). Since they have multi-layered designs, they perform high-level inference in data analysis. This research has predicted temperature values using the Dendritic Neuron Model-Based Explainable Feedback Deep Artificial Neural Network (DeepDenT) architecture. The study consists of 412 monthly maximum temperature data covering 1991 to 2022 from the Giresun province. According to the results, the AutoDeepDenT method obtains more accurate predictions than all other tested models. This highlights the effectiveness of advanced deep learning techniques in temperature prediction and their importance for environmental sustainability.
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