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Forecasting Air Pollution Index in Relation to Stubble Burning in Punjab Using Machine Learning and Genetic Algorithm


Affiliations
1 AP, Khalsa College, Amritsar, India
2 Assistant Professor, Khalsa College, Amritsar, India
 

Tons of stubble is generated as residue after harvesting wheat and paddy agriculture crops. Farmers burn the stubble to dispose of it because there is not enough time for the next crop and take it as a quick and inexpensive solution. It harms our ecology and eco-system in numerous ways. For the survival of humanity, it is crucial right now in India to track and forecast the Air Quality Index (AQI). The most major and dangerous air contaminant in this area is particulate matter (PM). For making predictions, machine learning (ML) technology is more effective than earlier conventional methods. Numerous ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), classification methods, Regression analysis, etc., were widely used for maximum prediction. But here by using Random Forest with Genetic Algorithm (GA) a hybrid approach prediction takes place much better. In order to improve the output of the data-adaptive computation, GA was used. Presently, data on air pollution from the preceding five years have been analyzed and forecasted for a study on Punjab's key cities to estimate and forecast PM concentrations. It was examined how PM concentrations vary with the seasons and some air pollutants. Variable importance ranking (VIR) was used to assess the effectiveness of the presented model. Here, the main emphasis was on taking into account some of the data sets from important cities in Punjab for the prediction of ambient pollution and air quality by using machine learning with genetic algorithm. Various common metrics were used to compare the results of all the strategies.

Keywords

Particulate Matter (PM), Air Quality Index (AQI), Correlation Analysis, Machine Learning (ML), Variable Importance Ranking (VIR), Random Forests (RF), Support Vector Regression (SVR).
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  • Abdurrahman, Muhammad Isa, Sukalpaa Chaki, and Gaurav Saini. "Stubble burning: Effects on health & environment, regulations and management practices," Environmental Advances 2 (2020)

  • Singh, Jabrinder, Naveen Singhal, Shailey Singhal, Madhu Sharma, Shilpi Agarwal, and Shefali Arora. "Environmental implications of rice and wheat stubble burning in north-western states of India," In Advances in Health and Environment safety, pp. 47-55. Springer, Singapore, 2018

  • Pöschl, Ulrich. "Atmospheric aerosols: composition, transformation, climate and health effects," Angewandte Chemie International Edition 44, no. 46 (2005): 7520-7540.

  • Kam, Winnie, Kalam Cheung, Nancy Daher, and Constantinos Sioutas. "Particulate matter (PM) concentrations in underground and ground-level rail systems of the Los Angeles Metro," Atmospheric Environment 45, no. 8 (2011): 1506-1516.

  • Shaughnessy, William J., Mohan M. Venigalla, and David Trump. "Health effects of ambient levels of respirable particulate matter (PM) on healthy, young-adult population," Atmospheric environment 123 (2015): 102-111.

  • Feng, Shaolong, Dan Gao, Fen Liao, Furong Zhou, and Xinming Wang. "The health effects of ambient PM2. 5 and potential mechanisms," Ecotoxicology and environmental safety 128 (2016): 67-74.

  • Shereen, Muhammad Adnan, Suliman Khan, Abeer Kazmi, Nadia Bashir, and Rabeea Siddique. "COVID-19 infection: Emergence, transmission, and characteristics of human coronaviruses," Journal of advanced research 24 (2020): 91-98.

  • Ali, Muhammad Ubaid, Siyi Lin, Balal Yousaf, Qumber Abbas, Mehr Ahmed Mujtaba Munir, Audil Rashid, Chunmiao Zheng, Xingxing Kuang, and Ming Hung Wong. "Pollution characteristics, mechanism of toxicity and health effects of the ultrafine particles in the indoor environment: Current status and future perspectives," Critical Reviews in Environmental Science and Technology 52, no. 3 (2022): 436-473.

  • Nagar, Pavan K., and Mukesh Sharma. "A hybrid model to improve WRF-Chem performance for crop burning emissions of PM2. 5 and secondary aerosols in North India," Urban Climate 41 (2022): 101084.

  • Kaur, Hardeep, and Manvendra Singh. "An Assessment of Environmental Pollution and Policy Initiatives in Punjab, India: A Review," (2022)

  • Tiwari, Rajesh Kumar, and Tapan Kumar Dey. "A Novel Approach for Analysis of Air Quality Index Before and After Covid-19 Using Machine Learning," In Applications of Artificial Intelligence and Machine Learning, pp. 139-149. Springer, Singapore, 2022

  • Jaiswal, Harsh, Aman Singh, Akhilesh Chauhan, and Dicksha Sharma. "Air Quality Index Prediction using Machine Learning," (2022)

  • Kumar, Parmod. "Energy Generation by Use of Crop Stubble in Punjab," In Climate Change Challenge (3C) and Social-Economic-Ecological Interface-Building, pp. 507-518. Springer, Cham, 2016

  • Bellinger, Colin, Mohomed Shazan Mohomed Jabbar, Osmar Zaïane, and Alvaro Osornio-Vargas. "A systematic review of data mining and machine learning for air pollution epidemiology," BMC public health 17, no. 1 (2017): 1-19.

  • Zalakeviciute, Rasa, Yves Rybarczyk, Jesús López-Villada, and Maria Valeria Diaz Suarez. "Quantifying decade-long effects of fuel and traffic regulations on urban ambient PM2. 5 pollution in a mid-size South American city," Atmospheric Pollution Research 9, no. 1 (2018): 66-75.

  • Sharma, Nidhi, Shweta Taneja, Vaishali Sagar, and Arshita Bhatt. "Forecasting air pollution load in Delhi using data analysis tools," Procedia computer science 132 (2018): 1077-1085.

  • Sweileh, Waleed M., Samah W. Al-Jabi, Sa’ed H. Zyoud, and Ansam F. Sawalha. "Outdoor air pollution and respiratory health: a bibliometric analysis of publications in peer-reviewed journals (1900–2017)," Multidisciplinary Respiratory Medicine 13, no. 1 (2018): 1-12.

  • Dua, Radhika Dua, Divyam Madaan Madaan, Prerana Mukherjee Mukherjee, and Brejesh Lall Lall. "Real time attention based bidirectional long short-term memory networks for air pollution forecasting," In 2019 IEEE fifth international conference on Big Data computing service and applications (BigDataService), pp. 151-158. IEEE, 2019

  • Kumar, K., and B. P. Pande. "Air pollution prediction with machine learning: a case study of Indian cities," International Journal of Environmental Science and Technology (2022): 1-16.

  • Mahalingam, Usha, Kirthiga Elangovan, Himanshu Dobhal, Chocko Valliappa, Sindhu Shrestha, and Giriprasad Kedam. "A machine learning model for air quality prediction for smart cities," In 2019 International conference on wireless communications signal processing and networking (WiSPNET), pp. 452-457. IEEE, 2019

  • Singh, Anish, Raja Kumar, and Nitasha Hasteer. "Comparative Analysis of Classification Models for Predicting Quality of Air," In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 7-11. IEEE, 2020

  • Castelli, Mauro, Fabiana Martins Clemente, Aleš Popovič, Sara Silva, and Leonardo Vanneschi. "A machine learning approach to predict air quality in California," Complexity 2020 (2020)

  • Bamrah, Sumneet Kaur, K. R. Saiharshith, and K. S. Gayathri. "Application of Random Forests for Air quality estimation in India by adopting terrain features," In 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP), pp. 1- 6. IEEE, 2020

  • Kumar, Saurabh, Shweta Mishra, and Sunil Kumar Singh. "A machine learning-based model to estimate PM2. 5 concentration levels in Delhi's atmosphere," Heliyon 6, no. 11 (2020): e05618.

  • Harishkumar, K. S., K. M. Yogesh, and Ibrahim Gad. "Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models," Procedia Computer Science 171 (2020): 2057-2066.

  • Liang, Yun-Chia, Yona Maimury, Angela Hsiang-Ling Chen, and Josue Rodolfo Cuevas Juarez. "Machine learning-based prediction of air quality," Applied Sciences 10, no. 24 (2020): 9151.

  • Madan, Tanisha, Shrddha Sagar, and Deepali Virmani. "Air quality prediction using machine learning algorithms–a review," In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), pp. 140-145. IEEE, 2020

  • Madhuri, R., S. Sistla, and K. Srinivasa Raju. "Application of machine learning algorithms for flood susceptibility assessment and risk management," Journal of Water and Climate Change 12, no. 6 (2021): 2608-2623.

  • Monisri, P. R., R. K. Vikas, N. K. Rohit, M. C. Varma, and B. N. Chaithanya. "Prediction and analysis of air quality using machine learning," Int J Adv Sci Technol 29, no. 5 (2020): 6934-6943.

  • Patil, Miss Priti Ashok, and Ms Ashwini Salunkhe. "Comparative analysis of construction cost estimation using artificial neural networks," J Xidian Univ 14 (2020): 1287-305.

  • Chhapariya, Koushikey, Anil Kumar, and Priyadarshi Upadhyay. "A fuzzy machine learning approach for identification of paddy stubble burnt fields," Spatial Information Research 29, no. 3 (2021): 319-329.

  • Sanjeev, Dyuthi. "Implementation of machine learning algorithms for analysis and prediction of air quality," International Journal of Engineering Research & Technology (IJERT) 10, no. 3 (2021): 533-538.

  • Arif, M., K. K. Alghamdi, S. A. Sahel, S. O. Alosaimi, M. E. Alsahaft, M. A. Alharthi, and M. Arif. "Role of machine learning algorithms in forest fire management: a literature review," J Robotics Autom 5, no. 1 (2021): 212-226.

  • Barthwal, Anurag, Debopam Acharya, and Divya Lohani. "Prediction and analysis of particulate matter (PM2. 5 and PM10) concentrations using machine learning techniques," Journal of Ambient Intelligence and Humanized Computing (2021): 1-16.

  • Kaur, Rajveer, and Puneeta Pandey. "Air pollution, climate change, and human health in Indian cities: a brief review," Frontiers in Sustainable Cities 3 (2021): 705131.

  • Pardasani, Raghav. "Rethinking Rice Cultivation: A Multiple Regression Analysis of Factors Influencing the Prevalence of Stubble Burning in Punjab,", 2021

  • Keil, Alwin, P. P. Krishnapriya, Archisman Mitra, Mangi L. Jat, Harminder S. Sidhu, Vijesh V. Krishna, and Priya Shyamsundar. "Changing agricultural stubble burning practices in the Indo-Gangetic plains: is the Happy Seeder a profitable alternative?," International Journal of Agricultural Sustainability 19, no. 2 (2021): 128-151.

  • Sangwan, V., and S. Deswal. "PM 2.5 modelling during paddy stubble burning months using artificial intelligence techniques," Journal of Achievements in Materials and Manufacturing Engineering 110, no. 1 (2022)

  • Pant, Alka, Sanjay Sharma, Mamta Bansal, and Mandeep Narang. "Comparative Analysis of Supervised Machine Learning Techniques for AQI Prediction," In 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 1-4. IEEE,

  • Aruna, Ch, B. Sai Charitha, G. Mahitha, Ch Bhuvana, D. Bhargavi, and D. Lakshmi Mounika. "Agri-Stubble Aggregation and Disposal," Volume 5, Issue 6, June 2022

  • Bhawan, Parivesh, and East Arjun Nagar. "Central Pollution Control Board," (2020)

  • Kumar, Rajesh, Suresh K. Sharma, J. S. Thakur, P. V. M. Lakshmi, M. K. Sharma, and T. Singh. "Association of air pollution and mortality in the Ludhiana city of India: a time-series study," Indian journal of public health 54, no. 2 (2010): 98.

  • Gupta, Shreekant, Shalini Saksena, and Omer F. Baris. "Environmental enforcement and compliance in developing countries: Evidence from India," World Development 117 (2019): 313-327.

  • Liu, Hui, and Chao Chen. "Prediction of outdoor PM2. 5 concentrations based on a three-stage hybrid neural network model," Atmospheric Pollution Research 11, no. 3 (2020): 469-481.

  • Biancofiore, Fabio, Marcella Busilacchio, Marco Verdecchia, Barbara Tomassetti, Eleonora Aruffo, Sebastiano Bianco, Sinibaldo Di Tommaso, Carlo Colangeli, Gianluigi Rosatelli, and Piero Di Carlo. "Recursive neural network model for analysis and forecast of PM10 and PM2. 5," Atmospheric Pollution Research 8, no. 4 (2017): 652-659.

  • Mirjalili, Seyedali. "Genetic algorithm," In Evolutionary algorithms and neural networks, pp. 43-55. Springer, Cham, 2019

  • Gregorutti, Baptiste, Bertrand Michel, and Philippe Saint-Pierre. "Correlation and variable importance in random forests," Statistics and Computing 27, no. 3 (2017): 659-678.


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  • Forecasting Air Pollution Index in Relation to Stubble Burning in Punjab Using Machine Learning and Genetic Algorithm

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Authors

Dr. Rachhpal Singh
AP, Khalsa College, Amritsar, India
Dr. Rupinder Singh
Assistant Professor, Khalsa College, Amritsar, India
Prabhjot Kaur
Assistant Professor, Khalsa College, Amritsar, India

Abstract


Tons of stubble is generated as residue after harvesting wheat and paddy agriculture crops. Farmers burn the stubble to dispose of it because there is not enough time for the next crop and take it as a quick and inexpensive solution. It harms our ecology and eco-system in numerous ways. For the survival of humanity, it is crucial right now in India to track and forecast the Air Quality Index (AQI). The most major and dangerous air contaminant in this area is particulate matter (PM). For making predictions, machine learning (ML) technology is more effective than earlier conventional methods. Numerous ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), classification methods, Regression analysis, etc., were widely used for maximum prediction. But here by using Random Forest with Genetic Algorithm (GA) a hybrid approach prediction takes place much better. In order to improve the output of the data-adaptive computation, GA was used. Presently, data on air pollution from the preceding five years have been analyzed and forecasted for a study on Punjab's key cities to estimate and forecast PM concentrations. It was examined how PM concentrations vary with the seasons and some air pollutants. Variable importance ranking (VIR) was used to assess the effectiveness of the presented model. Here, the main emphasis was on taking into account some of the data sets from important cities in Punjab for the prediction of ambient pollution and air quality by using machine learning with genetic algorithm. Various common metrics were used to compare the results of all the strategies.

Keywords


Particulate Matter (PM), Air Quality Index (AQI), Correlation Analysis, Machine Learning (ML), Variable Importance Ranking (VIR), Random Forests (RF), Support Vector Regression (SVR).

References