Exploring machine learning in the context of environmental usage prediction

Sweta Kumari; Syed Shahid Raza; Gopal Arora; Shambhu Bharadwaj

doi:10.31893/multiscience.2024ss0503

Keywords:

Environment

weather prediction

Bumble Bee Mating Optimization (BBMO)

Naïve Bayes Algorithm (NBA)

Abstract

The use of environmental prediction refers to predicting the impact that human activity will have on ecosystems, natural resources and other environmental factors in the future. This strategy looks at historical patterns, present situations and future predictions to hypothesize about the ecological effects of human activities, climate change and other factors. This research suggests machine learning(ML) techniques to predict environmental uses. Prediction accuracy declinesover time and models face challenges due to the need for observable data integration in sectors like agriculture, energy and waterfor successful sub-seasonal predictions. To tackle these issues, we proposed a Next Generation Bumble Bee Mating Optimized Naïve Bayes Algorithm (NGBBMO-NBA) method that is used to enhance weather prediction. The research gathers the SSF dataset to make predictions on the usage of the environment. We use a min-max normalization approach for data preprocessing. The principalcomponent analysis (PCA) method extracts features from the SSF data. Environmental uncertainty inhibits sub-seasonal projections. Our suggested method, NGBBMO-NBA, surpasses the current techniques for ecological prediction in terms of energy consumption (96.5%), F1-Score (96%), Mean Absolute Error (MAE) (97) and Root Mean Square Error (RMSE) (98.5).
References
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23. Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Khan, I. (2021). Environmental air pollution management system: Predicting user adoption behavior of big data analytics. Technology in Society, 64, 101473. https://doi.org/10.1016/j.techsoc.2020.101473
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Kumari, S., Raza, S. S., Arora, G., & Bharadwaj, S. (2024). Exploring machine learning in the context of environmental usage prediction. Multidisciplinary Science Journal, 6, 2024ss0503. https://doi.org/10.31893/multiscience.2024ss0503

[1] Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., & Younis, I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539-42559. https://doi.org/10.1007/s11356-022-19718-6

[2] Ali, A., Hussain, T., Tantashutikun, N., Hussain, N., & Cocetta, G. (2023). Application of smart techniques, Internet of Things and data mining for resource use efficient and sustainable crop production. Agriculture, 13(2), 397. https://doi.org/10.3390/agriculture13020397

[3] Andries, A., Morse, S., Murphy, R. J., Lynch, J., & Woolliams, E. R. (2022). Using data from earth observation to support sustainable development indicators: An analysis of the literature and challenges for the future. Sustainability, 14(3), 1191. https://doi.org/10.3390/su14031191

[4] Bozdağ, A., Dokuz, Y., & Gökçek, Ö. B. (2020). Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey. Environmental Pollution, 263, 114635. https://doi.org/10.1016/j.envpol.2020.114635

[5] Branny, A., Møller, M. S., Korpilo, S., McPhearson, T., Gulsrud, N., Olafsson, A. S., Raymond, C. M., & Andersson, E. (2022). Smarter greener cities through a social-ecological-technological systems approach. Current Opinion in Environmental Sustainability, 55, 101168. https://doi.org/10.1016/j.cosust.2022.101168

[6] Casson, A., Beghi, R., Giovenzana, V., Fiorindo, I., Tugnolo, A., & Guidetti, R. (2020). Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness. Biosystems Engineering, 189, 1-10. https://doi.org/10.1016/j.biosystemseng.2019.11.003

[7] Feng, Y., Wang, J., Wang, N., & Chen, C. (2023). Alert-based wearable sensing system for individualized thermal preference prediction. Building and Environment, 232, 110047. https://doi.org/10.1016/j.buildenv.2023.110047

[8] Floreano, I. X., & de Moraes, L. A. (2021). Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, 193(4), 239. https://doi.org/10.1007/s10661-021-09016-y

[9] He, S., Li, X., DelSole, T., Ravikumar, P., & Banerjee, A. (2021). Sub-seasonal climate forecasting via machine learning: Challenges, analysis, and advances. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 1, pp. 169-177). https://doi.org/10.1609/aaai.v35i1.16090

[10] Jayasingh, S. K., Mantri, J. K., & Pradhan, S. (2022). Smart weather prediction using machine learning. In Intelligent Systems: Proceedings of ICMIB (pp. 571-583). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0901-6_50

[11] Kareem, S., Hamad, Z. J., & Askar, S. (2021). An evaluation of CNN and ANN in prediction weather forecasting: A review. Sustainable Engineering and Innovation, 3(2), 148-159. https://doi.org/10.37868/sei.v3i2.id146

[12] Kaur, G., & Bala, A. (2021). OPSA: An optimized prediction-based scheduling approach for scientific applications in cloud environment. Cluster Computing, 1-20. https://doi.org/10.1007/s10586-021-03232-4

[13] Kumar, J., & Singh, A. K. (2021). Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment. Applied Soft Computing, 113, 107895. https://doi.org/10.1016/j.asoc.2021.107895

[14] Luo, M., Jiang, K., Wang, J., Feng, W., Ma, L., Shi, X., & Zhou, X. (2022). Data-driven thermal preference prediction model with embodied air-conditioning sensors and historical usage behaviors. Building and Environment, 220, 109269. https://doi.org/10.1016/j.buildenv.2022.109269

[15] Mohammad Amini, M., Jesus, M., Fanaei Sheikholeslami, D., Alves, P., Hassanzadeh Benam, A., & Hariri, F. (2023). Artificial intelligence ethics and challenges in healthcare applications: A comprehensive review in the context of the European GDPR mandate. Machine Learning and Knowledge Extraction, 5(3), 1023-1035. https://doi.org/10.3390/make5030053

[16] Ngiam, K. Y., & Khor, W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262-e273. https://doi.org/10.1016/S1470-2045(19)30149-4

[17] Paukert, C., Olden, J. D., Lynch, A. J., Breshears, D. D., Christopher Chambers, R., Chu, C., Daly, M., Dibble, K. L., Falke, J., Issak, D., & Jacobson, P. (2021). Climate change effects on North American fish and fisheries to inform adaptation strategies. Fisheries, 46(9), 449-464. https://doi.org/10.1002/fsh.10668

[18] Reddy, N. M., Saravanan, S., Almohamad, H., & Al Dughairi, A. A., & Abdo, H. G. (2023). Effects of climate change on streamflow in the Godavari basin simulated using a conceptual model including CMIP6 dataset. Water, 15(9), 1701. https://doi.org/10.3390/w15091701

[19] Reis, G. B., da Silva, D. D., Fernandes Filho, E. I., Moreira, M. C., Veloso, G. V., de Souza Fraga, M., & Pinheiro, S. A. (2021). Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow. Journal of Environmental Management, 290, 112625. https://doi.org/10.1016/j.jenvman.2021.112625

[20] Savari, M., & Gharechaee, H. (2020). Application of the extended theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. Journal of Cleaner Production, 263, 121512. https://doi.org/10.1016/j.jclepro.2020.121512

[21] Schuyler, Q., Wilcox, C., Lawson, T. J., Ranatunga, R. R., Hu, C. S., & Hardesty, B. D. (2021). Human population density is a poor predictor of debris in the environment. Frontiers in Environmental Science, 9, 133. https://doi.org/10.3389/fenvs.2021.583454

[22] Shah, M. I., Javed, M. F., Alqahtani, A., & Aldrees, A. (2021). Environmental assessment based surface water quality prediction using hyper-parameter optimized machine learning models based on consistent big data. Process Safety and Environmental Protection, 151, 324-340. https://doi.org/10.1016/j.psep.2021.05.026

[23] Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Khan, I. (2021). Environmental air pollution management system: Predicting user adoption behavior of big data analytics. Technology in Society, 64, 101473. https://doi.org/10.1016/j.techsoc.2020.101473

[24] Zhong, B., Da Silva, R. L., Tran, M., Huang, H., & Lobaton, E. (2021). Efficient environmental context prediction for lower limb prostheses. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(6), 3980-3994. https://doi.org/10.1109/TSMC.2020.2993399

Abstract

References

How to cite