339
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction

, ORCID Icon &
Pages 192-208 | Received 14 Jul 2023, Accepted 22 Feb 2024, Published online: 04 Mar 2024

References

  • Abdullah, A. S., Ruchjana, B. N., Jaya, I. G. N. M., (2021). Comparison of SARIMA and SVM model for rainfall forecasting in Bogor city, Indonesia. Journal of Physics: Conference Series, 1722(1), 012061. doi:10.1088/1742-6596/1722/1/012061
  • Abebe, W. T., & Endalie, D. (2023). Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in Ethiopia. Journal of Big Data, 10(1), 2. doi:10.1186/s40537-022-00683-3
  • Ahmadi, F., Radmanesh, F., & Mir Abbasi Najafabadi, R. (2013). Comparison of genetic programming methods and support vector machine in predicting daily river flow in barandozchai river. Soil and Water Research, 28, 1171–1162.
  • Amisigo, B. A., Van de Giesen, N., Rogers, C., Andah, W. E. I., & Friesen, J. (2008). Monthly streamflow prediction in the volta basin of West Africa: A SISO NARMAX polynomial modelling. Physics and Chemistry of the Earth, 33(1–2), 141–15. doi:10.1016/j.pce.2007.04.019
  • Arriagada, P., Dieppois, B., Sidibe, M., & Link, O. (2019). Impacts of climate change and climate variability on hydropower potential in data-scarce regions subjected to multi-decadal variability. Energies, 12(14), 2747. doi:10.3390/en12142747
  • Arselan, C. A. (2012). Stream flow simulation and synthetic flow calculation by modified Thomas Fiering model. Al-Rafidain Engineering Journal, 20(4), 118–127. doi:10.33899/rengj.2012.54160
  • Azadi, S., Nozari, H., & Godarzi, E. (2020). Predicting sediment load using stochastic model and rating curves in a hydrological station. Journal of Hydrologic Engineering, 25(8), 05020017. doi:10.1061/(ASCE)HE.1943-5584.0001967
  • Azadi, M., Taghizadeh, E., Memarian, M. H., & Dmitrieva-Arrago, L. R. (2013). Comparing the results of precipitation forecast based on mesoscale models on the territory of Iran during the cold season. Russian Meteorology and Hydrology, 38(9), 605–613. doi:10.3103/S1068373913090033
  • Bashar, A. M., Nozari, H., Marofi, S., Mohamadi, M., & Ahadiiman, A. (2023). Investigation of factors affecting rural drinking water consumption using intelligent hybrid models. Water Science and Engineering, 16(2), 175–183. doi:10.1016/j.wse.2022.12.002
  • Behzad, M., Asghari, K., & Coppola, E. A., Jr. (2010). Comparative study of SVMs and ANNs in aquifer water level prediction. Journal of Computing in Civil Engineering, 24(5), 408–413. doi:10.1061/(ASCE)CP.1943-5487.0000043
  • Benimam, H., Si-Moussa, C., Laidi, M., & Hanini, S. (2020). Modeling the activity coefficient at infinite dilution of water in ionic liquids using artificial neural networks and support vector machines. Neural Computing & Applications, 32(12), 8635–8653. doi:10.1007/s00521-019-04356-w
  • Cercignani, C. (1988). The Boltzmann equation. In the Boltzmann equation and its applications (pp. 40–103). New York: Springer.
  • Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., & Kis¸i, Ö. (2016). Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61(6), 1001–1009. doi:10.1080/02626667.2014.966721
  • Danandeh Mehr, A., Nourani, V., Karimi Khosrowshahi, V., & Ghorbani, M. A. (2019). A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology, 16(1), 335–346. doi:10.1007/s13762-018-1674-2
  • Dawoodi, H. H. (2021). Rainfall prediction in north Maharashtra region using support vector machine. TURCOMAT, 12(7), 1501–1505.
  • Du, J., Liu, Y., Yu, Y., & Yan, W. (2017). A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms. Algorithms, 10(2), 57. doi:10.3390/a10020057
  • Galelli, S., & Castelletti, A. (2013). Tree‐based iterative input variable selection for hydrological modeling. Water Resources Research, 49(7), 4295–4310. doi:10.1002/wrcr.20339
  • Giang, N. H., Wang, Y., Hieu, T. D., Phuong, L. A., & Thinh, N. T. (2022). Monthly precipitation prediction using neural network algorithms in the thua Thien hue province. Journal of Water and Climate Change, 13(5), 2011–2033. doi:10.2166/wcc.2022.271
  • Hamidi, O., Poorolajal, J., Sadeghifar, M., Abbasi, H., Maryanaji, Z., Faridi, H. R., & Tapak, L. (2015). A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theoretical and Applied Climatology, 119(3–4), 723–731. doi:10.1007/s00704-014-1141-z
  • Iranian Water Resources Management Company. 2022. Data center. Available from: http://wrbs.wrm.ir.
  • Kisi, O., & Cimen, M. (2012). Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Applications of Artificial Intelligence, 25(4), 783–792. doi:10.1016/j.engappai.2011.11.003
  • Kisi, O., & Ozkan, C. (2017). A new approach for modeling sediment-discharge relationship: Local weighted linear regression. Water Resources Management, 31(1), 1–23. doi:10.1007/s11269-016-1481-9
  • Lin, S. S., Zhang, N., Zhou, A., & Shen, S. L. (2022). Time-series prediction of shield movement performance during tunneling based on hybrid model. Tunn Undergr Space Technol, 119, 104245. doi:10.1016/j.tust.2021.104245
  • Markuna, S., Kumar, P., Ali, R., Vishwkarma, D. K., Kushwaha, K. S. … Kuriqi, A. (2023). Application of innovative machine learning techniques for long-term rainfall prediction. Geofisica Pura E Applicata, 180(1), 335–363. doi:10.1007/s00024-022-03189-4
  • Moharana, L., Sahoo, A., & Ghose, D. K. (2022). Prediction of rainfall using hybrid SVM-HHO model. IOP conf. Ser Earth Environmental Sciences, 1084(1), 012054. doi:10.1088/1755-1315/1084/1/012054
  • Nasir, H. A., & Weyer, E. (2016). System identification of the upper part of Murray River. Control Engineering Practice, 52, 70–92. doi:10.1016/j.conengprac.2016.04.006
  • Novak, D. R., Bailey, C., Brill, K. F., Burke, P., Hogsett, W. A., Rausch, R., & Schichtel, M. (2014). Precipitation and temperature forecast performance at the weather prediction center. Weather Forecast, 29(3), 489–504. doi:10.1175/WAF-D-13-00066.1
  • Nozari, H., & Tavakoli, F. (2020). Forecasting hydrologic parameters using linear and nonlinear stochastic models. Journal of Water and Climate Change, 11(4), 1284–1301. doi:10.2166/wcc.2019.249
  • Ortiz-García, E. G., Salcedo-Sanz, S., & Casanova-Mateo, C. (2014). Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data. Atmospheric Research, 139, 128–136. doi:10.1016/j.atmosres.2014.01.012
  • Pai, P. F., & Hong, W. C. (2007). A recurrent support vector regression model in rainfall forecasting. Hydrological Processes: International Journal, 21(6), 819–827. doi:10.1002/hyp.6323
  • Park, K., Rothfeder, R., Petheram, S., Buaku, F., Ewing, R., & Greene, W. H. (2020). Linear regression. In R. Ewing & K. Park (Eds.), Basic Quantitative Research Methods for Urban Planners (pp. 220–269). New York: Routledge.
  • Parmar, A., Mistree, K., & Sompura, M., 2017. Machine learning techniques for rainfall prediction: A review. In International conference on innovations in information embedded and communication systems, Coimbatore, India, 17–18 March 2017.
  • Patra, T., Mitra, S. K., & Pinchera, D. (2020). Link Budget Analysis for 5G communication in the Tropical Regions. Wireless Communications and Mobile Computing, 2020, 1–9. doi:10.1155/2020/6669965
  • Pham, B. T., Le, L. M., Le, T. T., Bui, K. T. T., Le, V. M., Ly, H. B., & Prakash, I. (2020). Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research, 237, 104845. doi:10.1016/j.atmosres.2020.104845
  • Pouyanfar, S., Nozari, H., & Khodamorad Pour, M. (2023). Comparison of the performances of the gene expression programming model and the RegCM model in predicting monthly runoff. Journal of Water and Climate Change, 14(10), 3810–3829. doi:10.2166/wcc.2023.439
  • Rahimi, N., Arian, M., & Ghorashi, M. (2017). Active tectonics of the saymareh-karkheh river basin (Northwest of Persian Gulf, Iran). Open Journal of Marine Science, 7(2), 238–257. doi:10.4236/ojms.2017.72017
  • Reddy, P. C. S., Yadala, S., & Goddumarri, S. N. (2022). Development of rainfall forecasting model using machine learning with singular spectrum analysis. IIUM Engineering Journal, 23(1), 172–186. doi:10.31436/iiumej.v23i1.1822
  • Ren, Y., Hu, F., & Miao, H. (2016). The optimization of kernel function and its parameters for SVM in well-logging, 13th International Conference on Service Systems and Service Management (ICSSSM), Kunming, China, (pp. 1–5). ICSSSM., IEEE. doi: 10.1109/ICSSSM.2016.7538563
  • Rosen, S. L., & Harmonosky, C. M. (2005). An improved simulated annealing simulation optimization method for discrete parameter stochastic systems. Computers & Operations Research, 32(2), 343–358. doi:10.1016/S0305-0548(03)00240-5
  • Safari, M. J. S., Rahimzadeh Arashloo, S., & Danandeh Mehr, A. (2020). Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm. Journal of Hydrology, 587, 125014. doi:10.1016/j.jhydrol.2020.125014
  • Sehad, M., Lazri, M., & Ameur, S. (2017). Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery. Advances in Space Research, 59(5), 1381–1394. doi:10.1016/j.asr.2016.11.042
  • Shao, Q., & Li, M. (2013). An improved statistical analogue downscaling procedure for seasonal precipitation forecast. Stochastic Environmental Research and Risk Assessment, 27(4), 819–830. doi:10.1007/s00477-012-0610-0
  • Shenify, M., Danesh, A. S., Gocić, M., Taher, R. S., Abdul Wahab, A. W., Gani, A., & Petković, D. (2016). Precipitation estimation using support vector machine with discrete wavelet transform. Water Resource Management, 30(2), 641–652. doi:10.1007/s11269-015-1182-9
  • Sh, A., Khan, I. H., & Parida, B. P. (2001). Performance of stochastic approaches for forecasting river water quality. Water Research, 35(18), 4261–4266. doi:10.1016/S0043-1354(01)00167-1
  • Silvestro, F., & Rebora, N. (2014). Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic flood forecasting chain. Journal of Hydrology, 519, 1052–1067. doi:10.1016/j.jhydrol.2014.07.042
  • Stanton, J. M., & Galton, P. (2001). The peas: A brief history of linear regression for statistics instructors. Journal of Statistics Education: An International Journal on the Teaching and Learning of Statistics, 9(3), 1–13. doi:10.1080/10691898.2001.11910537
  • Subasi, A. (2013). Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Computers in Biology and Medicine, 43(5), 576–586. doi:10.1016/j.compbiomed.2013.01.020
  • Tanessong, R. S., Igri, P. M., Vondou, D. A., Tamo, P. H. K., & Kamga, F. M. (2014). Evaluation of probabilistic precipitation forecast determined from WRF forecasted amounts. Theoretical and Applied Climatology, 116(3–4), 649–659. doi:10.1007/s00704-013-0965-2
  • Tao, H., Sulaiman, S. O., Yaseen, Z. M., Asadi, H., Meshram, S. G., & Ghorbani, M. A. (2018). What is the potential of integrating phase space reconstruction with SVM-FFA data-intelligence model? Application of rainfall forecasting over regional scale. Water Resources Management, 32(12), 3935–3959. doi:10.1007/s11269-018-2028-z
  • Tran, N. H., & Tran, K. (2007). Combination of fuzzy ranking and simulated annealing to improve discrete fracture inversion. Mathematical and Computer Modelling of Dynamical Systems, 45(7–8), 1010–1020. doi:10.1016/j.mcm.2006.08.013
  • Wang, H., Shangguan, L., Wu, J., & Guan, R. (2013). Multiple linear regression modeling for compositional data. Neurocomputing, 122, 490–500. doi:10.1016/j.neucom.2013.05.025
  • Yan, X., & Su, X. (2009). Linear regression analysis: Theory and computing. World Scientific, Singapore: World Scientific Research.
  • Yu, P. S., Chen, S. T., & Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3–4), 704–716. doi:10.1016/j.jhydrol.2006.01.021
  • Zaini, N., Malek, M. A., Yusoff, M., Mardi, N. H., & Norhisham, S. (2018). Daily river flow forecasting with hybrid support vector machine–particle swarm optimization. IOP Conference Series: Earth and Environmental Science, 140, 012035. doi:10.1088/1755-1315/140/1/012035
  • Zhang, X., Chen, X., & He, Z. (2010). An ACO-based algorithm for parameter optimization of support vector machines. Expert Systems with Applications, 37(9), 6618–6628. doi:10.1016/j.eswa.2010.03.067