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A Random Forest Regressor Model for Forecasting Air Quality Index from Particulate Matters

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.12, No. 10)

Publication Date:

Authors : ; ;

Page : 57-70

Keywords : Air Quality Index; Air particulate Matters; Random Forest Model; Forecast; Air Pollution;

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Air pollution is a growing concern in urban areas, including Port Harcourt City, Nigeria. Air quality index (AQI) is a numerical scale that is used for accessing and analyzing day to day air quality pattern of any place. It is projected to express the probable severity of the adverse health impacts on living organisms, most especially humans. It is however particulate matters (PM) that pose the greatest risks human health and the immediate environment. In this dissertation, a Random Forest Regressor model was used to forecast Air Quality Index. The data of the average diurnal values of PM10 and PM2.5 from Rivers State Ministry of Environment, Port Harcourt City over a long period (2017 to 2022) was evaluated. Additional meteorological and environmental variables, such as temperature, humidity, wind speed, and precipitation, were also collected as features for the prediction model. Data preprocessing techniques (MinMax Scaler) were applied to ensure data quality and consistency. The Random Forest Regressor was trained using 80% of the dataset, and the remaining 20% was used for validation. The model was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R-squared (R^2). The results obtained showed that the Random Forest Regressor performed well in forecasting Air Quality lndex with a high R^2 value of 97% and low errors (MSE: 5.2, RMSE: 7.25, MAE: 3.54). The Air Quality Index outcome shows that PM10 and PM2.5 across Port Harcourt metropolis are observed to be more noticeable during the periods of dawn than the afternoon. The Air Quality Index results reveals areas with Hazardous, moderate, good, bad and very good air quality.

Last modified: 2023-11-09 01:45:56