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The release of toxic gases by industries, emissions from vehicles, and anincrease in the concentration of harmful gases and particulate matter in theatmosphere are all contributing factors to the deterioration of the quality ofthe air. Factors such as industries, urbanization, population growth, and theincreased use of vehicles contribute to the rapid increase in pollution levels,which can adversely impact human health. This paper presents a model forforecasting the air quality index in Nigeria using the Bi-directional LSTMmodel. The air pollution data was downloaded from an online database (UCL). Thedataset was pre-processed using both pandas tools in python. The pre-processedresult was used as input features in training a Bi-LSTM model in making futureforecasts of the values of the particulate matter Pm2.5, and Pm10. The Bi-LSTMmodel was evaluated using some evaluation parameters such as mean square error,mean absolute error, absolute mean square, and R^2 square. The result of theBi-LSTM shows a mean square error of 52.99%, relative mean square error of7.28%, mean absolute error of 3.4%, and R^2 square of 97%. The model. Thisshows that the model follows a seamless trend in forecasting the air quality inPort Harcourt, Nigeria.

A Model for Forecasting Air Quality Index in Port Harcourt Nigeria Using Bi-LSTM Algorithm