Predicting Temperatures in an Extreme Climatic Environment Using Hybrid Neural Networks: Evaluating Noise Robustness

Predicting maximum temperatures is crucial across many fields and industries, including medicine, agriculture, energy, and climate research. Researchers have not treated the prediction of maximum temperatures under severe artificial data disturbance in much detail. So, it has not yet been understood. This research aims to integrate an artificial neural network (ANN) with the Guaranteed Convergence Arithmetic Operation Algorithm (GCAOA) to forecast monthly maximum temperatures while ensuring robu