Abstract
This paper focuses on an intuitive and effective way of giving data analysis and predictions based on the quality of various environmental factors for India. The problem with the existing solutions/system is that no complete dataset is available which gives a complete and thorough analysis of all the environmental factors such as air, water, tree cover, and forest cover. We have collected authentic data from various government sources and performed operations on the combined datasets. We have performed ETL on the various raw datasets and then imported all the transformed datasets into the PowerBI database and created multiple dashboards which gave data analysis based on all the different factors. For predictions and forecasting we have used RStudio, K-means clustering, and ARIMA model. The dashboards support Natural Language Processing and the user can input their query in the form of a sentence and will get the required results. We have implemented vitiation analysis for air quality, water quality, forest cover, and tree cover in India and the system is ready to be scaled to give analysis for different countries of the world.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Reddy, P.C., Babu, A.S.: Survey on weather prediction using big data analystics. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE Xplore
Gore, R.W., Deshpande, D.S.: An approach for classification of health risks based on air quality levels. In: 2017 1st International Conference on (ICISIM). http://ieeexplore.ieee.org/document/8122148/
Yuheng, S., Yingchun, K., Jing, C.: Ecological water quality assessment system based on BP neural network. In: Information Technology and Mechatronics Engineering Conference (ITOEC), IEEE (2017). http://ieeexplore.ieee.org/document/8122350/
Taneja, S., Sharma, N., Oberoi, K.: Predicting trends in air pollution in Delhi using data mining. In: 2016 1st India International Conference on Information Processing (IICIP). http://ieeexplore.ieee.org/document/7975379
Kingsy, G.R., Manimegalai, R., Geetha, D.M.S.: Air pollution analysis using enhanced K-Means clustering algorithm for real time sensor data. In: 2016 IEEE Region 10 Conference (TENCON). http://ieeexplore.ieee.org/document/7848362/
Bhanuse, S.S., Kamble, S.D., Thakur, N.V., Patharkar, A.S.: Metadata based text mining for generation of side information. In: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, vol. 10, pp. 135–141. https://doi.org/10.15439/2017r86,acsis. ISSN 2300-596
de Freitas, M.B., Cavalcanti, G.D.C., Sabourin, R.: Transducer state prediction system for smart environment intelligent control. In: 2015 Brazilian Conference on Intelligent Systems (BRACIS)
Souza, F.T., Rabelo, W.S.: A data mining approach to study the air pollution induced by urban phenomena and the association with respiratory diseases. In: Natural Computation (ICNC) (2015)
Dhore, A., Byakude, A., Sonar, B., Waste, M.: Weather prediction using the data mining techniques. Int. Res. J. Eng. Technol. (IRJET) 4(5) (2017)
Yan, H., Liu, Y., Han, X.: An evaluation model of water quality based on DSA-ELM method. In: Optical Communications and Networks (ICOCN) (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tirpude, S., Karandikar, A., Welekar, R. (2020). An Approach for Environment Vitiation Analysis and Prediction Using Data Mining and Business Intelligence. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_34
Download citation
DOI: https://doi.org/10.1007/978-981-15-0077-0_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0076-3
Online ISBN: 978-981-15-0077-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)