Abstract
Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective forecasts with minimum time duration. The BiGRNN is an enhanced version of Gated Recurrent Unit (GRU) that follows the process of passing input via forward and backward neural network and the outputs are linked to the identical output layer. The BiGRNN technique includes several hyper-parameters and hence, the hyperparameter optimization process takes place using Bird Mating Optimizer (BMO). The design of BMO algorithm for hyperparameter optimization of the BiGRNN, particularly for weather forecast shows the novelty of the work. The BMO algorithm is used to set hyperparameters such as momentum, learning rate, batch size and weight decay. The experimental result the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLP-ANN and RBF-ANN methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively.
Highlights
Weather conditions like wind, temperature and humidity profoundly affects human livelihood
The experimental result the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLPANN and RBF-Artificial Neural Networks (ANN) methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively
With 24 h, the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLP-ANN and RBF-ANN methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively
Summary
Temperature and humidity profoundly affects human livelihood. Artificial Neural Networks (ANN) could be employed for their adaptive nature and learning abilities based on previous knowledge [5] Such feature makes the ANN technique most attractive in application domain to resolve highly non-linear phenomena. The Artificial Neural Network (ANN) is one of the robust data modelling tools which can represent and capture complicated relations between outputs and inputs. It is proposed by the inspiration of executing artificial system which can implement intelligent tasks like human brain. The Deep Neural Network (DNN) is a type of ANN made up of multilayer architectures that can recreate the raw datasets from the original feature space to a learned feature space. A wide range of simulation analysis take place on the benchmark dataset and the experimental results are investigated under various aspects
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