SMART ENVIRONMENT INDEX PREDICTION OF SMART CITY USING POLYNOMIAL REGRESSION
DOI:
https://doi.org/10.53808/KUS.2022.ICSTEM4IR.0130-seKeywords:
Smart environment index, smart city components, polynomial regression, feature selection, hyper-tuning, regression errorAbstract
Smart Environment refers to environment where pollution is detected, predicted, classified and solved using smart tools and technology such as using Internet of Things (IoT) sensors, cloud service, and machine learning algorithms. Episodic environment index prediction allows governments and local city agency to detect pollution in environment at a premature stage to initiate proper steps. Advancements in machine learning, sensor, and camera technology have endorsed us to do that, which can benefit millions of cities and its citizens indeed. This paper describes a new method for smart environment index prediction based on index of five others smart city components using polynomial regression. This research mainly works with the Smart Cities Index dataset. We attempted numerous steps to select feature and to filter its contents to make them more apposite for selected regression algorithm. Our approach unites several feature selection techniques, outlier detection technique, polynomial degree selection technique, and random state selection technique, which outcomes in a reduction of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to 0.0000000140, 0.000000000000000322, and 0.0000000179 respectively and rise R2 to 1.0 for prediction. This method can be a valuable tool for smart environment index prediction from smartness of various components of a city, thus drastically reducing survey or other related works.
Downloads
References
Akhilesh, K. B. (2020). Smart Technologies—Scope and Applications. In Smart Technologies (pp. 1-16). Springer, Singapore.
Alexopoulos, C., Pereira, G. V., Charalabidis, Y., & Madrid, L. (2019, April). A taxonomy of smart cities initiatives. In Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance. (pp. 281-290).
Araldo, A. (2020, February 16). In practice, when you write Python code, and you use PolynomialFeatures together with sklearn.linear_model.LinearRegression, the latter takes care by default. [Comment on the article “Scikit-learn Polynomial Features – What is the Use of the Include Bias option”.] https://stackoverflow.com/questions/59725907/scikit-learn-polynomialfeatures-what-is-the-use-of-the-include-bias-option
Benamrou, B., Mohamed, B., Bernoussi, A. S., & Mustapha, O. (2016, October). Ranking models of smart cities. In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt). (pp. 872-879). IEEE.
Bhoomika, K.N., Deepa, C., Rashmi, R.K. & Srinivasa, R. (2016). Internet of Things for Environmental Monitoring. International Journal of Advanced Networking & Applications. 497–501.
Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 326). John Wiley & Sons.
Drewil, G. I., & Al-Bahadili, R. J. (2021). Forecast Air Pollution in Smart City Using Deep Learning Techniques: A Review. Multicultural Education, 7(5). 38-47. https://doi.org/10.5281/zenodo. 4737746
Giffinger, R., Fertner, C., Kramar, H., & Meijers, E. (2007). City-ranking of European medium-sized cities. Cent. Reg. Sci. Vienna UT, 9, 1-12.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques (3rd ed.). Elsevier.
Haselirad, A. (2019, January 29). If there is no randomstate provided the system will use a randomstate that is generated internally. So, when you run. [Comment on the article "Random state (Pseudo-random number) in Scikit learn".] https://stackoverflow.com/questions/28064634/random-state-pseudo-random-number-in-scikit-learn
International Organization for Standardization. (2014). Sustainable development of communities: Indicators for city services and quality of life. ISO.
Introduction to Polynomial Regression. (2020). Analytics Vidhya. https://www.analyticsvidhya.com/blog/2020/03/ polynomial-regression-python/
Kang, G. K., Gao, J. Z., Chiao, S., Lu, S., & Xie, G. (2018). Air quality prediction: Big data and machine learning approaches. International Journal of Environmental Science and Development, 9(1), 8-16. https://doi.org/10.18178/ijesd.2018.9.1.1066
Kulkarni, P., & Akhilesh, K. B. (2020). Big data analytics as an enabler in smart governance for the future smart cities. In smart technologies (pp. 53-65). Springer, Singapore.
Machine Learning – Polynomial Regression (n.d.). W3schools. https://www.w3schools.com/python/python_ ml_polynomial_regression.asp
Polynomial Regression. (2022). Net-informations.com. http://net-informations.com/ds/mla/pr.htm
Polynomial Regression in Python using Scikit-learn. (2021). Data36. https://data36.com/polynomial-regression-python-scikit-learn/
Prefeitura Belo Horizonte. (2020). Cidade Inteligente. https://prefeitura.pbh.gov.br/cidade-inteligente
Pure Earth (n.d.). https://www.pureearth.org.
Singh, M. N., & Kumar, M. (2020). Big Data Analytics Based Methods For Addressing Various Issues Efficiently in Smart Cities: A Comprehensive Survey. International Journal of Advanced Science and Technology. 29(5s). 238-245
Smart Cities Index Datasets (2021). Kaggle. https://www.kaggle.com/magdamonteiro/smart-cities-index-datasets
Smart City Index Methodology (n.d.). https://www.imd.org/globalassets/wcc/docs/smart_city/smart_city_ index_methodology_and_groups.pdf
Smart City Observatory (n.d.). IMD. https://www.imd.org/smart-city-observatory/home/
Soomro, K., Bhutta, M. N. M., Khan, Z., & Tahir, M. A. (2019). Smart city big data analytics: An advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(5), e1319. https://doi.org /10.1002/widm.1319
TEIXEIRA, J. V. S., Gerais, M., BARACHO, B. R. M. A., & MULLARKEY, B. M. T. (2020). Proposal for Sustainable Smart City Indicators. Convention Proceedings: Papers presented at the 24th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2020). (pp. 120-125). International Institute of Informatics and Cybernetics.
Tran Thi Hoang, G., Dupont, L., & Camargo, M. (2019). Application of decision-making methods in smart city projects: a systematic literature review. Smart Cities, 2(3), 433-452. https://doi.org/10.3390/ smartcities2030027
Ullo, S. L., & Sinha, G. R. (2020). Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20(11), 3113. https://doi.org/10.3390/s20113113
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Khulna University Studies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
