Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate

Journal of Environmental Management

Volumn 222, Issue , 2018, Pages 190-206

  Zeynoddin, Mohammad ^a   Bonakdari, Hossein ^a   Azari, Arash ^a   Ebtehaj, Isa ^a   Gharabaghi, Bahram ^b   Riahi Madavar, Hossein ^c  

^a RAZI UNIVERSITY   (Iran)

^b UNIVERSITY OF GUELPH   (Canada)

^c VALI E ASR UNIVERSITY OF RAFSANJAN   (Iran)

Author keywords

Forecast; Hybrid model; Rainfall; Spectral analysis; Tropical climate; Uncertainty

Indexed keywords

RAIN;

DATA PROCESSING; FUZZY MATHEMATICS; MACHINE LEARNING; METHODOLOGY; MONTE CARLO ANALYSIS; NUMERICAL METHOD; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; RAINFALL; STOCHASTICITY; TROPICAL ENVIRONMENT; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

ARTICLE; BACK PROPAGATION; COMPARATIVE STUDY; CONCEPTUAL FRAMEWORK; FORECASTING; FUZZY SYSTEM; HYBRID; MACHINE LEARNING; MONTE CARLO METHOD; NONLINEAR SYSTEM; STOCHASTIC MODEL; TIME SERIES ANALYSIS; TROPIC CLIMATE; WATERSHED; WAVELET TRANSFORMATION; ALGORITHM; FUZZY LOGIC; STATISTICAL MODEL;

ALGORITHMS; FORECASTING; FUZZY LOGIC; LINEAR MODELS; MONTE CARLO METHOD; RAIN; TROPICAL CLIMATE;

EID: 85047460584     PISSN: 03014797     EISSN: 10958630     Source Type: Journal    
DOI: 10.1016/j.jenvman.2018.05.072     Document Type: Article

References (55)

1. Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., Srinivasan, R., Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. 333:2–4 (2007), 413–430 https://doi.org/10.1016/j.jhydrol.2006.09.014.
2. Adamowski, J., Chan, H.F., Prasher, S.O., Sharda, V.N., Comparison of multivariate adaptive regression splines with coupled wavelet transforms artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J. Hydroinf. 14:3 (2012), 731–744 https://doi.org/10.2166/hydro.2011.044.
3. Awan, J.A., Bae, D.H., Improving ANFIS based model for long-term dam inflow prediction by incorporating monthly rainfall forecasts. Water Resour. Manag. 28:5 (2014), 1185–1199 https://doi.org/10.1007/s11269-014-0512-7.
4. Azimi, H., Bonakdari, H., Ebtehaj, I., Michelson, D.G., A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Comput. Appl., 2016 https://doi.org/10.1007/s00521-016-2560-9.
5. Babel, M.S., Sirisena, T.A.J.G., Singhrattna, N., Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand. Hydrol. Res. 48 (2017), 867–882 https://doi.org/10.2166/nh.2016.212.
6. Bliemel, F., Theil's forecast accuracy coefficient: a clarification. J. Market. Res. 10:4 (1973), 444–446.
7. Box, G.E.P., Cox, D.R., An analysis of transformations. J. Roy. Stat. Soc. B 26 (1964), 211–252.
8. Box, G.E.P., Jenkins, G.M., Series Analysis Forecasting and Control. first ed., 1976, Holden-Day, San Francisco, 575 ISBN-10: 0816211043.
9. Camporeale, C., Perona, P., Porporato, A., Ridolfi, L., The hierarchy of models for meandering rivers and related morphodynamic processes. Rev. Geophys., 45(1), 2007, RG1001 https://doi.org/10.1029/2005RG000185.
10. Cryer, J., Chan, K., Time Series Analysis. 2008, Springer-Verlag, New York, USA.
11. Department of Irrigation and Drainage (DID), Annual Flooding Report of Pahang State 2005., 2005, Department of Irrigation and Drainage Malaysia (DID Malaysia).
12. Department of Irrigation and Drainage (DID), Annual Flooding Report of Pahang State 2008/2009., 2009, Department of Irrigation and Drainage Malaysia (DID Malaysia).
13. Devak, M., Dhanya, C.T., Gosain, A.K., Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall. J. Hydrol. 525 (2015), 286–301 https://doi.org/10.1016/j.jhydrol.2015.03.051.
14. Ebtehaj, I., Bonakdari, H., A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes. Water Sci. Technol. 73 (2016), 2244–2250 https://doi.org/10.2166/wst.2016.064.
15. Ebtehaj, I., Bonakdari, H., Gharabaghi, B., Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement 116 (2018), 473–482 https://doi.org/10.1016/j.measurement.2017.11.023.
16. Ebtehaj, I., Bonakdari, H., Shamshirband, S., Extreme learning machine assessment for estimating sediment transport in open channels. Eng. Comput. 32 (2016), 691–704 https://doi.org/10.1007/s00366-016-0446-1.
17. Gazendam, E., Gharabaghi, B., Ackerman, J., Whiteley, H., Integrative neural networks models for stream assessment in restoration projects. J. Hydrol. 536 (2016), 339–350 https://doi.org/10.1016/j.jhydrol.2016.02.057.
18. Ghorbani, M.A., Deo, R.C., Karimi, V., Yaseen, Z.M., Terzi, O., Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey. Stoch. Environ. Res. Risk A, 2017 https://doi.org/10.1007/s00477-017-1474-0.
19. Heo, K.Y., Ha, K.J., Yun, K.S., Lee, S.S., Kim, H.J., Wang, B., Methods for uncertainty assessment of climate models and model predictions over East Asia. Int. J. Climatol. 34 (2013), 377–390 https://doi.org/10.1002/joc.3692.
20. Hong, W.C., Pai, P.F., Potential assessment of the support vector regression technique in rainfall forecasting. Water Resour. Manag. 21 (2007), 495–513 https://doi.org/10.1007/s11269-006-9026-2.
21. Huang, G.B., Zhu, Q.Y., Siew, C.K., Extreme learning machine: a new learning scheme of feedforward neural networks. Proceedings of the International Joint Conference on Neural Networks, vol. 2, 2004, 985–990 https://doi.org/10.1109/IJCNN.2004.1380068.
22. Huang, G.B., Zhu, Q.Y., Siew, C.K., Extreme learning machine: theory and applications. Neurocomputing 70 (2006), 489–501 https://doi.org/10.1016/j.neucom.2005.12.126.
23. Hurst, H.E., Black, R.P., Simaika, Y.M., Long-term Storage: an Experimental Study. 1965, Constable, London.
24. Jabatan Meteorologi Malaysia (JMM), Monsun. Portal Rasmi Jabatan Meteorologi Malaysia. 2010 http://www.met.gov.my/index.php?option=%20com_content&task=view&id=69&Itemid=160&limit=1&limitstart=0 Jabatan Meteorologi Malaysia.
25. Jian, L., Zhao, Y., Zhu, Y.P., Zhang, M.B., Bertolatti, D., An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci. Total Environ. 426 (2012), 336–345 https://doi.org/10.1016/j.scitotenv.2012.03.025.
26. John, J., Draper, N., An alternative family of transformations. J. Roy. Stat. Soc. C App. 29 (1980), 190–197 https://doi.org/10.2307/2986305.
27. Johnson, N., Systems of frequency curves generated by methods of translation. Biometrika 36 (1949), 149–176 https://doi.org/10.2307/2332539.
28. Kumar, A., Goyal, P., Forecasting of daily air quality index in Delhi. Sci. Total Environ. 409:24 (2011), 5517–5523 https://doi.org/10.1016/j.scitotenv.2011.08.069.
29. Luk, K.C., Ball, J.E., Sharma, A., An application of artificial neural networks for rainfall forecasting. Math. Comput. Model. 33 (2001), 683–693 https://doi.org/10.1016/S0895-7177(00)00272-7.
30. Manly, B.F., Exponential data transformations. Statistician 25 (1976), 37–42 https://doi.org/10.2307/2988129.
31. Mazari, M., Rodriguez, D.D., Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. J. Traffic Transport. Eng. 3 (2016), 448–455 https://doi.org/10.1016/j.jtte.2016.09.007.
32. Mehdizadeh, S., Behmanesh, J., Khalili, K., New approaches for estimation of monthly rainfall based on GEP-ARCH and ANN-ARCH hybrid models. Water Resour. Manag., 2017 https://doi.org/10.1007/s11269-017-1825-0.
33. Mirzavand, M., Ghazavi, R., A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water Resour. Manag. 29 (2015), 1315–1328 https://doi.org/10.1007/s11269-014-0875-9.
34. Modarres, R., Ouarda, T.B.M.J., Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series. Hydrol. Process. 27 (2013), 3174–3191.
35. Moeeni, H., Bonakdari, H., Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model. Stoch. Environ. Res. Risk A 31 (2017), 1997–2010 https://doi.org/10.1007/s00477-016-1273-z.
36. Moeeni, H., Bonakdari, H., Ebtehaj, I., Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. J. Earth Syst. Sci., 126(18), 2017 https://doi.org/10.1007/s12040-017-0798-y.
37. Moeeni, H., Bonakdari, H., Ebtehaj, I., Integrated SARIMA with neuro-fuzzy systems and neural networks for monthly inflow prediction. Water Resour. Manag. 31 (2017), 2141–2156 https://doi.org/10.1007/s11269-017-1632-7.
38. Moeeni, H., Bonakdari, H., Fatemi, S.E., Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction. J. Hydrol 547 (2017), 348–364 https://doi.org/10.1016/j.jhydrol.2017.02.012.
39. Ng, J.L., Aziz, S.A., Huang, Y.F., Wayayok, A., Rowshon, M.K., Stochastic modelling of seasonal and yearly rainfalls with low-frequency variability. Stoch. Environ. Res. Risk A 31 (2017), 2215–2233 https://doi.org/10.1007/s00477-016-1373-9.
40. Nieto, P.G., Lasheras, F.S., García-Gonzalo, E., de Cos Juez, F.J., PM 10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: a case study. Sci. Total Environ. 621 (2018), 753–761 https://doi.org/10.1016/j.scitotenv.2017.11.291.
41. Osarumwense, O.I., Applicability of box Jenkins SARIMA model in rainfall forecasting: a case study of Port-Harcourt south south Nigeria. Can. J. Comput. Math. Nat. Sci. Eng. Med. 4 (2013), 1–4.
42. Pektaş, A.O., Cigizoglu, H.K., ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. J. Hydrol. 500 (2013), 21–36 https://doi.org/10.1016/j.jhydrol.2013.07.020.
43. Phukoetphim, P., Shamseldin, A.Y., Adams, K., Multimodel approach using neural networks and symbolic regression to combine the estimated discharges of rainfall-runoff models. J. Hydrol. Eng., 21, 2016, 04016022 https://doi.org/10.1061/(ASCE)HE.1943-5584.0001332.
44. Salas, J.D., Delleur, J.W., Yevjevich, V.M., Lane, W.L., Applied Modeling of Hydrologic Time Series. 1980, Water Resources Publications, Littleton.
45. Shi, B., Wang, P., Jiang, J., Liu, R., Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies. Sci. Total Environ. 610 (2018), 1390–1399 https://doi.org/10.1016/j.scitotenv.2017.08.232.
46. Taylor, K.E., Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 106 (2001), 7183–7192 https://doi.org/10.1029/2000JD900719.
47. Tehrany, M.S., Pradhan, B., Jebur, M.N., Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch. Environ. Res. Risk A 29 (2015), 1149–1165 https://doi.org/10.1007/s00477-015-1021-9.
48. Theil, H., Economic Forecasts and Policy. 1961, North-Holland Pub. Co.
49. Theil, H., Applied Economic Forecasting. 1966, North-Holland Pub. Co.
50. Valipour, M., Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol. Appl. 22 (2015), 592–598 https://doi.org/10.1002/met.1491.
51. Valipour, M., Banihabib, M.E., Behbahani, S.M.R., Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476 (2013), 433–441 https://doi.org/10.1016/j.jhydrol.2012.11.017.
52. Wang, W.C., Chau, K.W., Xu, D.M., Chen, X.Y., Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour. Manag. 29 (2015), 2655–2675 https://doi.org/10.1007/s11269-015-0962-6.
53. Yaseen, Z.M., Ghareb, M.I., Ebtehaj, I., Bonakdari, H., Siddique, R., Heddam, S., Yusif, A.A., Deo, R., Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water Resour. Manag., 2018 https://doi.org/10.1007/s11269-017-1797-0.
54. Yeo, I.K., Johnson, R.A., A new family of power transformation to improve normality or symmetry. Biometrika 87 (2000), 954–959 https://doi.org/10.1093/biomet/87.4.954.
55. Zeroual, A., Meddi, M., Assani, A.A., Artificial neural network rainfall-discharge model assessment under rating curve uncertainty and monthly discharge volume predictions. Water Resour. Manag. 30 (2016), 3191–3205 https://doi.org/10.1007/s11269-016-1340-8.