Machine Learning Techniques to Predict Rainfall of Vidarbh Region
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Abstract
Accurate daily rainfall forecasting is vital for proper agricultural planning and managing resources efficiently. This study evaluates the performance of various methods, with an emphasis on temperature and precipitation, such as linear regression, random forest, SVM regression, and XGBoost. Linear regression displays minimal RMSE and a flawless R-squared, whereas random forest performs effectively with low RMSE and encouraging R2 results. However, the presence of a negative R-squared indicates potential overfitting. The MAE, MSE, and RMSE statistics for SVM are competitive. The study draws attention to the unexplored Vidarbha region, which includes 11 districts, using Nagpur district as a representative instance. Additionally, future plans involve the utilization of deep learning models like ARIMA and LSTM to enhance rainfall prediction accuracy across Vidarbha. This investigation yields valuable insights into climate prediction, offering support for well-informed decision-making.
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References
Ehsan, Muhammad Azhar & Tippett, Michael & Robertson, Andrew & Almazroui, Mansour & Ismail, Muhammad & Dinku, Tufa & Acharya, Nachiketa & Siebert, Asher & Ahmed, Jemal Seid & Teshome, Asaminew. (2021). Seasonal predictability of Ethiopian Kiremt rainfall and forecast skill of ECMWF's SEAS5 model. Climate Dynamics. 57. 10.1007/s00382-021-05855-0.
Ahmed, Syed & Sharma, Rohit & Goel, Pradeep & Khan, Alamgir & Gharabaghi, Bahram & Rudra, Ramesh. (2022). A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow. Hydrology. 9. 133. 10.3390/hydrology9080133.
Shukla, Siddhartha & Kumar, Sandeep & Sharma, Navdeep & Bhatia, Dr. (2022). Data Mining Application: Rainfall Predictions. International Journal for Research in Applied Science and Engineering Technology. 10. 113-118. 10.22214/ijraset.2022.47833.
Dada, Emmanuel & Yakubu, Joseph & Oyewola, David. (2021). Artificial Neural Network Models for Rainfall Prediction. European Journal of Electrical Engineering and Computer Science. 5. 10.24018/ejece.2021.5.2.313.
Singh, Vikrant. (2020). Study of Various Rainfall Estimation Prediction Techniques using Data Mining. International Journal of Engineering Research and. V9. 10.17577/IJERTV9IS070464.
Liyew, C.M., Melese, H.A. Machine learning techniques to predict daily rainfall amount. J Big Data 8, 153 (2021). https://doi.org/10.1186/s40537-021-00545-4
Alefu Chinasho, Bobe Bedadi, Tesfaye Lemma, Tamado Tana, Tilahun Hordofa, & Bisrat Elias. (2021). Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia. Advances in Meteorology, 2021, Article ID 9657460, 15 pages. https://doi.org/10.1155/2021/9657460
Mauro Castelli, Fabiana Martins Clemente, Aleš Popovič, Sara Silva, Leonardo Vanneschi, "A Machine Learning Approach to Predict Air Quality in California", Complexity, vol. 2020, Article ID 8049504, 23 pages, 2020. https://doi.org/10.1155/2020/8049504
Rahman, A. U., Abbas, S., Gollapalli, M., Ahmed, R., Aftab, S., Ahmad, M., Khan, M. A., & Mosavi, A. (2022). Rainfall Prediction System Using Machine Learning Fusion for Smart Cities. Sensors (Basel), 22(9), 3504. https://doi.org/10.3390/s22093504.
Wanie M. Ridwan, Michelle Sapitang, Awatif Aziz, Khairul Faizal Kushiar, Ali Najah Ahmed, Ahmed El-Shafie,Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia,Ain Shams Engineering Journal,Volume 12, Issue 2,2021,Pages 1651-1663,ISSN 2090-4479,https://doi.org/10.1016/j.asej.2020.09.011.
R Praveena, T R Ganesh Babu, M Birunda, G Sudha, P Sukumar, and J Gnanasoundharam. "Prediction of Rainfall Analysis Using Logistic Regression and Support Vector Machine." In Journal of Physics: Conference Series, Volume 2466, 4th National Conference on Communication Systems (NCOCS 2022), 23/12/2022 - 23/12/2022, Karaikal, India. Published under licence by IOP Publishing Ltd. 2023. J. Phys.: Conf. Ser. 2466 012032. DOI: 10.1088/1742-6596/2466/1/012032.
Moulana Mohammed, Roshitha Kolapalli, Niharika Golla, Siva Sai Maturi. "Prediction Of Rainfall Using Machine Learning Techniques." International Journal of Scientific & Technology Research, Volume 9, Issue 01, January 2020, ISSN 2277-8616. www.ijstr.org.
Necesito, Imee V., Donghyun Kim, Young Hye Bae, Kyunghun Kim, Soojun Kim, and Hung Soo Kim. 2023. "Deep Learning-Based Univariate Prediction of Daily Rainfall: Application to a Flood-Prone, Data-Deficient Country" Atmosphere 14, no. 4: 632. https://doi.org/10.3390/atmos14040632.
Sethupathi, G., Ganesh, Y.S., & Ali, M.M. (2021). Efficient Rainfall Prediction and Analysis using Machine Learning Techniques. Turkish Journal of Computer and Mathematics Education, 12(6), 3467-3474
Kadama, S., Jadhava, N., Mardane, V., & Kamble, N. (2022). Rainfall Prediction Using Machine Learning. International Journal of Research Publication and Reviews, 3(5), 2936-2944. ISSN 2582-7421. Retrieved from www.ijrpr.com.
Sarker, Iqbal. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science. 2. 10.1007/s42979-021-00815-1.
Omotunde, Ayokunle & Ogunsanwo, Gbenga & Adekola, Olubukola & Izang, Aaron & Abel, Samuel. (2023). COMPARATIVE ANALYSIS OF DATA MINING TECHNIQUES FOR MOVIE PREDICTION. FUDMA JOURNAL OF SCIENCES. 6. 224-228. 10.33003/fjs-2022-0606-1117.
Tian, Mi & Fan, Hao & Xiong, Zimin & Li, Lihua. (2023). Data-driven ensemble model for probabilistic prediction of debris-flow volume using Bayesian model averaging. Bulletin of Engineering Geology and the Environment. 82. 10.1007/s10064-022-03050-x.
Kumar, Vikas & Yadav, Vishal & Dubey, Er. (2022). Rainfall Prediction using Machine Learning. International Journal for Research in Applied Science and Engineering Technology. 10. 2494-2497. 10.22214/ijraset.2022.42876.
Nigam, Archana & Srivastava, Sanjay. (2023). Hybrid deep learning models for traffic stream variables prediction during rainfall. Multimodal Transportation. 2. 100052. 10.1016/j.multra.2022.100052.
Rainfall prediction using machine learning & deep learning techniques. Proceedings of the International
Conference on Electronics and Sustainable Communication Systems (ICESC 2020). Middlesex University: IEEE Xplore. 2020; pp. 92–97.
Preethi, B.Meena & Gowtham, R. & Aishvarya, S. & Karthick, S. & Sabareesh, D.G. (2021). Rainfall Prediction using Machine Learning and Deep Learning Algorithms. International Journal of Recent Technology and Engineering (IJRTE). 10. 251-254. 10.35940/ijrte. D6611.1110421.
Singh, Vikrant. (2020). Study of Various Rainfall Estimation Prediction Techniques using Data Mining. International Journal of Engineering Research and. V9. 10.17577/IJERTV9IS070464.
Pallava, Leela & Kovvur, Ram & Sheela, Sai & Mukirala, Dheeraj & Vivek, V. (2022). Prediction of Rainfall Using Different Machine Learning Regression Models. 10.1007/978-981-19-3391-2_16.
Lingwal, Surabhi & Bhatia, Komal & Singh, Manjeet. (2022). A novel machine learning approach for rice yield estimation. Journal of Experimental & Theoretical Artificial Intelligence. 1-20. 10.1080/0952813X.2022.2062458.