Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.
Chaitanya Baliram Pande, Nand Lal Kushwaha, Omer A Alawi, Saad Sh Sammen, Lariyah Mohd Sidek, Zaher Mundher Yaseen, Subodh Chandra Pal, Okan Mert Katipoğlu
Author Information
Chaitanya Baliram Pande: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq. Electronic address: chaitanay45@gmail.com.
Nand Lal Kushwaha: Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, Punjab, 141004, India; Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Omer A Alawi: Department of Thermofluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia.
Saad Sh Sammen: Department of Civil Engineering, College of Engineering, Diyala University, Diyala Governorate, Iraq.
Lariyah Mohd Sidek: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia.
Zaher Mundher Yaseen: Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Subodh Chandra Pal: Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India.
Okan Mert Katipoğlu: Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan Binali Yıldırım University, 24100, Erzincan, Turkey.
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
