Optimized Weather Forecasting using Enhanced Multilayer Perceptron for Seaweed Cultivation

Seaweed is considered a high-value source of protein for humans and cattle. In this work proposed classification model used to predict the production of seaweed K.alvarezii cultivation based on seasons by weather forecasting using Enhanced MultiLayer Perceptron (MLP) Neural Network. In this MLP neural network model was built to evaluate future predictions based on attributes that are outlook, temperature, and humidity from end of training data. The dataset was collected from Kaggle for classification and classified using Enhanced Multilayer Perceptron is implemented and its performance are evaluated. The MLP function has time taken to test a model on training data 0.42 seconds, enhanced MLP gives 0.09 seconds by increasing number of neurons in hidden layer.