Hybrid noise reduction-based data-driven modeling of relative humidity in Khulna, Bangladesh.

In this study, a hybrid Machine Learning (ML) approach is proposed for Relative Humidity (RH) prediction with a combination of Empirical Mode Decomposition (EMD) to improve the prediction accuracy over the traditional prediction technique using a Machine Learning (ML) algorithm called Support Vector Machine (SVM). The main objective of proposing this hybrid technique is to deal with the extremely nonlinear and noisy humidity pattern in Khulna, Bangladesh, which is experiencing rapid urbanization and environmental change. To develop the model, data on temperature, relative humidity, rainfall, and wind speed were collected from the Bangladesh Meteorological Department (BMD), and the data was divided into three phases: 70 % of the historical dataset as training data for the model, 15 % of the data set as the validation phase, and the remaining 15 % of the data set as the test phase of the model. Employing the Particle Swarm Optimization (PSO) algorithm, the SVM model determines its best hypermeters within this research. In the present research, performance analysis is carried out utilizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). Results show that the increase in R2 values resulting from the EMD-based approach is significant: 21.05 % in H1(Traditional model), 19.48 % in H2 (Traditional model), 76.92 % in H3 (Traditional model), 55.93 % in H4 (Traditional model), and 64.29 % in H5 (Traditional model) and H6 (Traditional model). The analytical results show that the proposed EMD-based technique efficiently filters and processes noisy, highly nonlinear humidity data during prediction in the Khulna region. It is recommended that this technique could be applied to other geological areas.