1. Home
  2. Vol 10, No 5 (2024)
  3. Buathongkhue

Analysis and Prediction of Rainfall with Oceanic Nino Index and Climate Variables Using Correlation Coefficient and Deep Learning

Chayanat Buathongkhue, Kritsana Sureeya, Natapon Kaewthong

Abstract


This article presents the relationship between the Oceanic Nino Index (ONI) and monthly rainfall on the southern and eastern coast of Thailand, specifically in Narathiwat, Pattani, and Yala provinces, where influences have been commonly observed. This research aims to study the relationship between the Oceanic Nino Index (ONI) and monthly rainfall to develop a model for predicting monthly rainfall. Despite previous related research, there has been no in-depth study on the relationship between the Oceanic Nino Index (ONI) and monthly rainfall in areas adjacent to the sea. The correlation coefficient was used to determine the relationship, revealing that the ONI value is significantly correlated with the amount of rainfall in the current month and the following month. This correlation paved the way for developing a model to predict monthly rainfall. Multiple linear regression, recurrent neural networks, and long short-term memory models were employed for this purpose. The study found that utilizing a recurrent neural network yielded the best prediction efficiency, with Mean Absolute Error (MAE) values of 112.76 mm for Narathiwat province, 81.06 mm for Pattani province, and 97.67 mm for Yala province.

 

Doi: 10.28991/CEJ-2024-010-05-01

Full Text: PDF


Keywords


Rainfall Prediction; Oceanic Nino Index; Eastern Sea Coast; Deep Learning.

References


Cai, W., Ng, B., Geng, T., Jia, F., Wu, L., Wang, G., Liu, Y., Gan, B., Yang, K., Santoso, A., Lin, X., Li, Z., Liu, Y., Yang, Y., Jin, F. F., Collins, M., & McPhaden, M. J. (2023). Anthropogenic impacts on twentieth-century ENSO variability changes. Nature Reviews Earth and Environment, 4(6), 407–418. doi:10.1038/s43017-023-00427-8.

Cheng, L., Abraham, J., Trenberth, K. E., Boyer, T., Mann, M. E., Zhu, J., Wang, F., Yu, F., Locarnini, R., Fasullo, J., Zheng, F., Li, Y., Zhang, B., Wan, L., Chen, X., Wang, D., Feng, L., … Lu, Y. (2024). New Record Ocean Temperatures and Related Climate Indicators in 2023. Advances in Atmospheric Sciences, 41(6), 1068–1082. doi:10.1007/s00376-024-3378-5.

Glantz, M. H., & Ramirez, I. J. (2020). Reviewing the Oceanic Niño Index (ONI) to Enhance Societal Readiness for El Niño’s Impacts. International Journal of Disaster Risk Science, 11(3), 394–403. doi:10.1007/s13753-020-00275-w.

L’Heureux, M. L., Tippett, M. K., Wheeler, M. C., Nguyen, H., Narsey, S., Johnson, N., Hu, Z. Z., Watkins, A. B., Lucas, C., Ganter, C., Becker, E., Wang, W., & Di Liberto, T. (2024). A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate. Journal of Climate, 37(4), 1197–1211. doi:10.1175/JCLI-D-23-0406.1.

Prasetyo, Y., & Nabilah, F. (2017). Pattern Analysis of El Nino and la Nina Phenomenon Based on Sea Surface Temperature (SST) and Rainfall Intensity using Oceanic Nino Index (ONI) in West Java Area. IOP Conference Series: Earth and Environmental Science, 98(1), 12041. doi:10.1088/1755-1315/98/1/012041.

Varotsos, C., Sarlis, N. V., Mazei, Y., Saldaev, D., & Efstathiou, M. (2024). A Composite Tool for Forecasting El Niño: The Case of the 2023–2024 Event. Forecasting, 6(1), 187–203. doi:10.3390/forecast6010011.

Silva, K. A., de Souza Rolim, G., & de Oliveira Aparecido, L. E. (2022). Forecasting El Niño and La Niña events using decision tree classifier. Theoretical and Applied Climatology, 148(3–4), 1279–1288. doi:10.1007/s00704-022-03999-5.

Wang, G. G., Cheng, H., Zhang, Y., & Yu, H. (2023). ENSO analysis and prediction using deep learning: A review. Neurocomputing, 520, 216–229. doi:10.1016/j.neucom.2022.11.078.

Bouach, A. (2024). Artificial neural networks for monthly precipitation prediction in north-west Algeria: a case study in the Oranie-Chott-Chergui basin. Journal of Water and Climate Change, 15(2), 582–592. doi:10.2166/wcc.2024.494.

Van Oldenborgh, G. J., Hendon, H., Stockdale, T., L’Heureux, M., Coughlan De Perez, E., Singh, R., & Van Aalst, M. (2021). Defining El Nio indices in a warming climate. Environmental Research Letters, 16(4), 44003. doi:10.1088/1748-9326/abe9ed.

Bochenek, B., & Ustrnul, Z. (2022). Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives. Atmosphere, 13(2), 180. doi:10.3390/atmos13020180.

Haggag, M., Siam, A. S., El-Dakhakhni, W., Coulibaly, P., & Hassini, E. (2021). A deep learning model for predicting climate-induced disasters. Natural Hazards, 107(1), 1009–1034. doi:10.1007/s11069-021-04620-0.

Kumar, V., Azamathulla, H. Md., Sharma, K. V., Mehta, D. J., & Maharaj, K. T. (2023). The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management. Sustainability, 15(13), 10543. doi:10.3390/su151310543.

Zhang, Y., Xie, D., Tian, W., Zhao, H., Geng, S., Lu, H., Ma, G., Huang, J., & Choy Lim Kam Sian, K. T. (2023). Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms. Remote Sensing, 15(3), 667. doi:10.3390/rs15030667.

Apipattanavis, S., Ketpratoom, S., & Kladkempetch, P. (2018). Water Management in Thailand. Irrigation and Drainage, 67(1), 113–117. doi:10.1002/ird.2207.

Maprasit, S., Pradabphetrat, P., Madmanang, R., Sathawong, S., Boonkaew, R., & Suksaroj, C. (2021). Physical-Chemical Properties Relationship of Pattani River and Implication for Water Quality Monitoring Study and Academic Service. Journal of Physics: Conference Series, 1835(1), 12112. doi:10.1088/1742-6596/1835/1/012112.

Nur Amyliyana Wan Faizurie Zaidee, W., Shakir Mohd Saudi, A., Khairul Amri Kamarudin, M., Ekhwan Toriman, M., Juahir, H., Fahmy Abu, I., Mahmud Nur Zahidah Shafii, M., Nizam, K., & Elfithri, R. (2018). Flood Risk Pattern Recognition Using Chemometric Techniques Approach in Golok River, Kelantan. International Journal of Engineering & Technology, 7(3.14), 75. doi:10.14419/ijet.v7i3.14.168655.

Hidayat, R., Ando, K., Masumoto, Y., & Luo, J. J. (2016). Interannual Variability of Rainfall over Indonesia: Impacts of ENSO and IOD and Their Predictability. IOP Conference Series: Earth and Environmental Science, 31(1), 12043. doi:10.1088/1755-1315/31/1/012043.

Irwandi, H., Pusparini, N., Ariantono, J. Y., Kurniawan, R., Tari, C. A., & Sudrajat, A. (2018). The Influence of ENSO to the Rainfall Variability in North Sumatra Province. IOP Conference Series: Materials Science and Engineering, 335(1), 12055. doi:10.1088/1757-899X/335/1/012055.

Ueangsawat, K., Nilsamranchit, S., & Jintrawet, A. (2015). Fate of ENSO Phase on Upper Northern Thailand, a Case Study in Chiang Mai. Agriculture and Agricultural Science Procedia, 5, 2–8. doi:10.1016/j.aaspro.2015.08.001.

Ahmed, H. A. Y., & Mohamed, S. W. A. (2021). Rainfall Prediction using Multiple Linear Regressions Model. 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan. doi:10.1109/iccceee49695.2021.9429650.

Shaker Reddy, P. C., & Sureshbabu, A. (2019). An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction. International Journal of Sensors, Wireless Communications and Control, 10(4), 473–483. doi:10.2174/2210327910666191218124350.

Agboola, A., Gabriel, A., Aliyu, E., & Alese, B. (2014). Development Of A Fuzzy Logic Based Rainfall Prediction Model. International Journal of Engineering & Technology, 3(4), 427–435.

Janarthanan, R., Balamurali, R., Annapoorani, A., & Vimala, V. (2021). Prediction of rainfall using fuzzy logic. Materials Today: Proceedings, 37, 959–963. doi:10.1016/j.matpr.2020.06.179.

Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. doi:10.1016/j.mlwa.2021.100204.

Rahman, A., 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, 22(9), 3504. doi:10.3390/s22093504.

Mohammadi, B. (2021). A review on the applications of machine learning for runoff modeling. Sustainable Water Resources Management, 7(6), 98. doi:10.1007/s40899-021-00584-y.

Singh, A. K., Kumar, P., Ali, R., Al-Ansari, N., Vishwakarma, D. K., Kushwaha, K. S., Panda, K. C., Sagar, A., Mirzania, E., Elbeltagi, A., Kuriqi, A., & Heddam, S. (2022). An Integrated Statistical-Machine Learning Approach for Runoff Prediction. Sustainability, 14(13), 8209. doi:10.3390/su14138209.

Hu, Z., Zhang, Y., Zhao, Y., Xie, M., Zhong, J., Tu, Z., & Liu, J. (2019). A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture. Sensors, 19(6), 1420. doi:10.3390/s19061420.

Melesse, A. M., Khosravi, K., Tiefenbacher, J. P., Heddam, S., Kim, S., Mosavi, A., & Pham, B. T. (2020). River water salinity prediction using hybrid machine learning models. Water (Switzerland), 12(10), 1–21. doi:10.3390/w12102951.

Rajabi-Kiasari, S., & Hasanlou, M. (2020). An efficient model for the prediction of SMAP sea surface salinity using machine learning approaches in the Persian Gulf. International Journal of Remote Sensing, 41(8), 3221–3242. doi:10.1080/01431161.2019.1701212.

Mosavi, A., Ozturk, P., & Chau, K. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10(11), 1536. doi:10.3390/w10111536.

Motta, M., de Castro Neto, M., & Sarmento, P. (2021). A mixed approach for urban flood prediction using Machine Learning and GIS. International Journal of Disaster Risk Reduction, 56, 102154. doi:10.1016/j.ijdrr.2021.102154.

Htike, K. K., & Khalifa, O. O. (2010). Rainfall forecasting models using focused time-delay neural networks. International Conference on Computer and Communication Engineering (ICCCE’10), Kuala Lumpur, Malaysia. doi:10.1109/iccce.2010.5556806.

Hong, W. C. (2008). Rainfall forecasting by technological machine learning models. Applied Mathematics and Computation, 200(1), 41–57. doi:10.1016/j.amc.2007.10.046.

Sivapragasam, C., Liong, S. Y., & Pasha, M. F. K. (2001). Rainfall and runoff forecasting with SSA-SVM approach. Journal of Hydroinformatics, 3(3), 141–152. doi:10.2166/hydro.2001.0014.

Feng, Q., Wen, X., & Li, J. (2015). Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions. Water Resources Management, 29(4), 1049–1065. doi:10.1007/s11269-014-0860-3.

Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. doi:10.1109/TNNLS.2021.3084827.

Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270. doi:10.1162/neco_a_01199.

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53(8), 5929–5955. doi:10.1007/s10462-020-09838-1.

Van, S. P., Le, H. M., Thanh, D. V., Dang, T. D., Loc, H. H., & Anh, D. T. (2020). Deep learning convolutional neural network in rainfall-runoff modelling. Journal of Hydroinformatics, 22(3), 541–561. doi:10.2166/hydro.2020.095.

Haidar, A., & Verma, B. (2018). Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network. IEEE Access, 6, 69053–69063. doi:10.1109/ACCESS.2018.2880044.

Van Viet, L. (2021). Development of a new ENSO index to assess the effects of ENSO on temperature over southern Vietnam. Theoretical and Applied Climatology, 144(3–4), 1119–1129. doi:10.1007/s00704-021-03591-3.

Asuero, A. G., Sayago, A., & González, A. G. (2006). The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry, 36(1), 41–59. doi:10.1080/10408340500526766.

Mardia, K. V. (1976). Linear circular correlation coefficients and rhythmometry. Biometrika, 63(2), 403–405. doi:10.2307/2335637.

Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. doi:10.1016/j.asoc.2019.105524.

Uyanık, G. K., & Güler, N. (2013). A Study on Multiple Linear Regression Analysis. Procedia - Social and Behavioral Sciences, 106, 234–240. doi:10.1016/j.sbspro.2013.12.027.

Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent Neural Networks for Time Series Forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. doi:10.1016/j.ijforecast.2020.06.008.

Medsker, L. R., & Jain, L. (2001). Recurrent neural networks. Design and Applications, 5(64-67), 2.

Lin, Y., Yan, Y., Xu, J., Liao, Y., & Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. North American Journal of Economics and Finance, 57, 101421. doi:10.1016/j.najef.2021.101421.

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. doi:10.1016/j.ejor.2017.11.054.

Apaydin, H., Feizi, H., Sattari, M. T., Colak, M. S., Shamshirband, S., & Chau, K. W. (2020). Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting. Water, 12(5), 1500. doi:10.3390/w12051500.


Full Text: PDF

DOI: 10.28991/CEJ-2024-010-05-01

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2024 Chayanat Buathongkhue, Kritsana Sureeya, Natapon Kaewthong

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message