ADVANCED MODELING FOR SEA LEVEL PRESSURE PREDICTION: A COMPARATIVE EVALUATION OF ANN AND ANFIS TECHNIQUES

Abstract

Pressure forecast plays a crucial role in weather forecasting, and this has a direct effect on the many fields including disaster management, agriculture, energy systems etc. The goal of this study is to compare the performances between ANN and ANFIS-based models for predicting around distribution over a range of different sea-level pressure values using various meteorological attributes as inputs. This study focuses on air temperature, wind speed, and humidity data sourced from the Macau Meteorological and Geophysical Office. We populated the dataset with missing values and performance metrics were used to train and test both models (RMSE, MAPE, R²). Overall results show that both models are good for Prediction but in accuracy, we can say that ANFIS is performing better of all the ANN types at RMSE and R² than others for Sea Level Pressure Forecasting. This increased accuracy can help in a wide variety of fields, from weather-related risk management and infrastructure planning to agricultural yield forecasting.

Keywords

• Artificial Neural Networks
• ANFIS
• Sea Level Pressure Forecasting

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