![]() ![]() The consistent rise in global temperatures over the twenty-first century continues to pose a serious challenge to humanity. This study concluded that the ANN model was more reliable for predicting relative humidity than SARIMA model. On the other hand, MLP model reported the forecasted relative humidity with RMSE of 4.65 and MAE of 3.42. The results showed that the SARIMA model provides the forecasted relative humidity with RMSE of 6.04 and MAE of 4.56. The accuracy of the models has been measured using root mean squared error (RMSE) and mean absolute error (MAE). The forecast trend in relative humidity declines from 2017 to 2025. The average monthly relative humidity data for the period 2000–2016 have been used to carry out the objectives of the proposed study. The present study attempted to implement seasonal autoregressive moving average (SARIMA) and artificial neural network (ANN) with multilayer perceptron (MLP) models to forecast the monthly relative humidity in Delhi, India during 2017–2025. Relative humidity plays an important role in climate change and global warming, making it a research area of greater concern in recent decades. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |