1,529
Views
16
CrossRef citations to date
0
Altmetric
Research Article

Sourcing CHIRPS precipitation data for streamflow forecasting using intrinsic time-scale decomposition based machine learning models

, , ORCID Icon & ORCID Icon
Pages 1437-1456 | Received 28 Dec 2020, Accepted 16 Apr 2021, Published online: 21 Jun 2021

References

  • Adamowski, J. and Sun, K., 2010. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390 (1–2), 85–91. doi:10.1016/j.jhydrol.2010.06.033
  • Adnan, R.M., et al., 2020. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 586, 124371. doi:10.1016/j.jhydrol.2019.124371
  • Adnan, R.M., et al., 2019. Daily streamflow prediction using optimally pruned extreme learning machine. Journal of Hydrology, 577, 123981. doi:10.1016/j.jhydrol.2019.123981
  • Anastasakis, L. and Mort, N., 2001. The development of self-organization techniques in modelling: a review of the Group Method of Data Handling (GMDH). UK: Department of Automatic Control & Systems Engineering, The University of Sheffield, Tech. Rep. 813.
  • Ashouri, H., et al., 2015. PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96 (1), 69–83. doi:10.1175/BAMS-D-13-00068.1
  • Band, S.S., et al., 2020. Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing, 12 (21), 3568. doi:10.3390/rs12213568
  • Beck, H.E., et al., 2017a. MSWEP: 3-hourly 0.25 global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrology and Earth System Sciences, 21 (1), 589–615. doi:10.5194/hess-21-589-2017
  • Beck, H.E., et al., 2017b. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21 (12), 6201. doi:10.5194/hess-21-6201-2017
  • Biau, G. and Scornet, E., 2016. A random forest guided tour. Test, 25 (2), 197–227. doi:10.1007/s11749-016-0481-7
  • Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:10.1023/A:1010933404324
  • Chiang, Y.M., Chang, L.C., and Chang, F.J., 2004. Comparison of static-feedforward and dynamic feedback neural networks for rainfall–runoff modeling. Journal of Hydrology, 290 (3–4), 297–311. doi:10.1016/j.jhydrol.2003.12.033
  • Cloke, H. and Pappenberger, F., 2009. Ensemble flood forecasting: a review. Journal of Hydrology, 375 (3–4), 613–626. doi:10.1016/j.jhydrol.2009.06.005
  • Dag, O. and Yozgatligil, C., 2016. GMDH: an R package for short term forecasting via GMDH-type neural network algorithms. The R Journal, 8 (1), 379–386. doi:10.32614/RJ-2016-028
  • Di, C., Yang, X., and Wang, X., 2014. A four-stage hybrid model for hydrological time series forecasting. PloS One, 9 (8), e104663. doi:10.1371/journal.pone.0104663
  • Famili, A., et al., 1997. Data preprocessing and intelligent data analysis. Intelligent Data Analysis, 1 (1), 3–23. doi:10.3233/IDA-1997-1102
  • Farlow, S.J., 1984. Self-organizing methods in modeling: GMDH type algorithms, Statistics: Textbooks and Monographs, Vol. 54. New York: Marcel Dekker.
  • Frei, M.G. and Osorio, I., 2006. Method, computer program, and system for intrinsic timescale decomposition, filtering, and automated analysis of signals of arbitrary origin or timescale, Available from: https://patents.google.com/patent/US7054792/en, US Patent 7054792B2.
  • Frei, M.G. and Osorio, I., 2007. Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 463 (2078), 321–342. doi:10.1098/rspa.2006.1761
  • Fu, M., et al., 2020. Deep learning data intelligence model based on adjusted forecasting window scale: application in daily streamflow simulation. IEEE Access, 8, 32632–32651. doi:10.1109/ACCESS.2020.2974406
  • Funk, C., et al., 2015. The climate hazards InfraRed precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2 (1), 1–21. doi:10.1038/sdata.2015.66
  • Gautam, D. and Holz, K.P., 2001. Rainfall-runoff modelling using adaptive neuro-fuzzy systems. Journal of Hydroinformatics, 3 (1), 3–10. doi:10.2166/hydro.2001.0002
  • Gorelick, N., et al., 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. doi:10.1016/j.rse.2017.06.031
  • Govindasamy, R., 1991. Univariate box-Jenkins forecasts of water discharge in Missouri river. International Journal of Water Resources Development, 7 (3), 168–177. doi:10.1080/07900629108722509
  • Han, J., Kamber, M., and Pei, J., 2012. Data preprocessing. In: J. Han, M. Kamber, and J. Pei, eds. Data mining. 3rd ed. Boston: Morgan Kaufmann, The Morgan Kaufmann Series in Data Management Systems, 83–124.
  • He, X., et al., 2019. Daily runoff forecasting using a hybrid model based on variational mode decomposition and deep neural networks. Water Resources Management, 33 (4), 1571–1590. doi:10.1007/s11269-019-2183-x
  • He, Z., et al., 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509, 379–386. doi:10.1016/j.jhydrol.2013.11.054
  • Hosseini, F.S., et al., 2020. Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method. Science of the Total Environment, 711, 135161. doi:10.1016/j.scitotenv.2019.135161
  • Hou, A.Y., et al., 2014. The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95 (5), 701–722. doi:10.1175/BAMS-D-13-00164.1
  • Hsu, K.L., Gupta, H.V., and Sorooshian, S., 1995. Artificial neural network modeling of the rainfall runoff process. Water Resources Research, 31 (10), 2517–2530. doi:10.1029/95WR01955
  • Huang, S., et al., 2014. Monthly streamflow prediction using modified EMD-based support vector machine. Journal of Hydrology, 511, 764–775. doi:10.1016/j.jhydrol.2014.01.062
  • Huffman, G.J., et al., 2007. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8 (1), 38–55. doi:10.1175/JHM560.1
  • Ivakhnenko, A.G. and Ivakhnenko, G.A., 1995. The review of problems solvable by algorithms of the Group Method of Data Handling (GMDH). Pattern Recognition and Image Analysis, 5, 527–535.
  • Jeihouni, M., Toomanian, A., and Mansourian, A., 2020. Decision tree-based data mining and rule induction for identifying high quality groundwater zones to water supply management: a novel hybrid use of data mining and GIS. Water Resources Management, 34 (1), 139–154. doi:10.1007/s11269-019-02447-w
  • Joyce, R.J., et al., 2004. CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5 (3), 487–503. doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2
  • Kao, I.F., et al., 2020. Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting. Journal of Hydrology, 583, 124631. doi:10.1016/j.jhydrol.2020.124631
  • Kass, G.V., 1980. An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29 (2), 119–127. doi:10.2307/2986296
  • Khatibi, R., Ghorbani, M.A., and Pourhosseini, F.A., 2017. Stream flow predictions using nature inspired firefly algorithms and a multiple model strategy–directions of innovation towards next generation practices. Advanced Engineering Informatics, 34, 80–89. doi:10.1016/j.aei.2017.10.002
  • Kirchner, J.W. and Allen, S.T., 2020. Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis. Hydrology and Earth System Sciences, 24, 17–39. doi:10.5194/hess-24-17-2020
  • Kisi, Ö., 2008. Stream flow forecasting using neuro-wavelet technique. Hydrological Processes: An International Journal, 22 (20), 4142–4152. doi:10.1002/hyp.7014
  • Krstanovic, P.F. and Singh, V.P., 1991. A univariate model for long-term streamflow forecasting. Stochastic Hydrology and Hydraulics, 5 (3), 189–205. doi:10.1007/BF01544057
  • Le, A.M. and Pricope, N.G., 2017. Increasing the accuracy of runoff and streamflow simulation in the Nzoia basin, Western Kenya, through the incorporation of satellite-derived CHIRPS data. Water, 9 (2), 114. doi:10.3390/w9020114
  • Li, X., Sha, J., and Wang, Z.L., 2019. Comparison of daily streamflow forecasts using extreme learning machines and the random forest method. Hydrological Sciences Journal, 64 (15), 1857–1866.
  • Liu, D., et al., 2020. Streamflow prediction using deep learning neural network: case study of Yangtze river. IEEE Access, 8, 90069–90086. doi:10.1109/ACCESS.2020.2993874
  • Louppe, G., 2014. Understanding random forests: from theory to practice. arXiv preprint, arXiv:1407.7502. Available from: https://arxiv.org/abs/1407.7502 [Accessed 14 November 2020].
  • Malik, A., et al., 2020. Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction. Stochastic Environmental Research and Risk Assessment, 34 (11), 1755–1773. doi:10.1007/s00477-020-01874-1
  • Mehr, A.D., 2018. An improved gene expression programming model for streamflow forecasting in intermittent streams. Journal of Hydrology, 563, 669–678. doi:10.1016/j.jhydrol.2018.06.049
  • Milanovic, M. and Stamenkovic, M., 2016. CHAID decision tree: methodological frame and application. Economic Themes, 54 (4), 563–586. doi:10.1515/ethemes-2016-0029
  • Moeeni, H., et al., 2017. Assessment of stochastic models and a hybrid artificial neural network-genetic algorithm method in forecasting monthly reservoir inflow. INAE Letters, 2 (1), 13–23. doi:10.1007/s41403-017-0017-9
  • Mohan, S. and Vedula, S., 1995. Multiplicative seasonal ARIMA model for long term forecasting of inflows. Water Resources Management, 9 (2), 115–126. doi:10.1007/BF00872463
  • Moradkhani, H. and Sorooshian, S., 2009. General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis. In: S. Sorooshian, et al., eds.. Hydrological modelling and the water cycle, Vol. 63. Berlin, Heidelberg: Springer, Water Science and Technology Library, 1–24.
  • Mujumdar, P.P. and Kumar, D.N., 1990. Stochastic models of streamflow: some case studies. Hydrological Sciences Journal, 35 (4), 395–410. doi:10.1080/02626669009492442
  • Nanda, T., et al., 2016. A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products. Journal of Hydrology, 539, 57–73. doi:10.1016/j.jhydrol.2016.05.014
  • Nayak, P.C., et al., 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291 (1–2), 52–66. doi:10.1016/j.jhydrol.2003.12.010
  • Nguyen, V.N., et al., 2020. A new modeling approach for spatial prediction of flash flood with biogeography optimized CHAID tree ensemble and remote sensing data. Remote Sensing, 12 (9), 1373. doi:10.3390/rs12091373
  • Noakes, D.J., McLeod, A.I., and Hipel, K.W., 1985. Forecasting monthly river flow time series. International Journal of Forecasting, 1 (2), 179–190. doi:10.1016/0169-2070(85)90022-6
  • Nourani, V., et al., 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358–377. doi:10.1016/j.jhydrol.2014.03.057
  • Onwubolu, G.C., 2016. GMDH multilayered algorithm. In: G.C. Onwubolu, ed.. GMDH-methodology and implementation in MATLAB. Massachusetts: Imperial College Press, 27–74.
  • Pai, D.S., et al., 2014. Development of a new high spatial resolution (0.25°×0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65 (1), 1–18.
  • Pavlov, Y.L., 2000. Random forests. Netherlands: VSP.
  • Rezaie-Balf, M., et al., 2020. Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: comparative assessment of a noise suppression hybridization approach. Journal of Cleaner Production, 271, 122576. doi:10.1016/j.jclepro.2020.122576
  • Rezaie-Balf, M., et al., 2019a. An ensemble decomposition-based artificial intelligence approach for daily streamflow prediction. Water, 11 (4), 709. doi:10.3390/w11040709
  • Rezaie-Balf, M., et al., 2019b. Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam. Hydrological Sciences Journal, 64 (13), 1629–1646. doi:10.1080/02626667.2019.1661417
  • Riggs, H.C., 1985. Applications of hydrologic data. In: H.C. Riggs, ed.. Streamflow characteristics, Vol. 22. Amsterdam, The Netherlands: Elsevier, Developments in Water Science, 207–237.
  • Ritschard, G., 2013. CHAID and earlier supervised tree methods. In: J.J. McArdle and G. Ritschard, eds. Contemporary issues in exploratory data mining in the behavioral sciences. New york: Routledge, 70–96.
  • Santos, C.A.G., et al., 2019. Hybrid wavelet neural network approach for daily inflow forecasting using tropical rainfall measuring mission data. Journal of Hydrologic Engineering, 24 (2), 04018062. doi:10.1061/(ASCE)HE.1943-5584.0001725
  • Scornet, E., 2015. Learning with random forests. Ph.D. thesis. Université Pierre et Marie Curie, Paris.
  • Shabri, A., and Suhartono, 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57 (7), 1275–1293. doi:10.1080/02626667.2012.714468
  • Sharma, A., Tarboton, D.G., and Lall, U., 1997. Streamflow simulation: a nonparametric approach. Water Resources Research, 33 (2), 291–308. doi:10.1029/96WR02839
  • Sivapragasam, C., Liong, S.Y., and Pasha, M., 2001. Rainfall and runoff forecasting with SSA–SVM approach. Journal of Hydroinformatics, 3 (3), 141–152. doi:10.2166/hydro.2001.0014
  • Statsoft, 2020. CHAID analysis. Available from: https://store.fmi.uni-sofia.bg/fmi/statist/education/textbook/eng/stchaid.html [Accessed 21 April 2020].
  • Sulugodu, B. and Deka, P.C., 2019. Evaluating the performance of CHIRPS satellite rainfall data for streamflow forecasting. Water Resources Management, 33 (11), 3913–3927. doi:10.1007/s11269-019-02340-6
  • Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106 (D7), 7183–7192. doi:10.1029/2000JD900719
  • Thomas, D. and Benson, M., 1970. Generalization of streamflow characteristics from drainage-basin characteristics. Washington, DC: US Department of the Interior, Tech. rep..
  • Tikhamarine, Y., et al., 2020. Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey wolf optimization (GWO) algorithm. Journal of Hydrology, 582, 124435. doi:10.1016/j.jhydrol.2019.124435
  • Tiwari, M.K. and Chatterjee, C., 2011. A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics, 13 (3), 500–519. doi:10.2166/hydro.2010.142
  • Tuo, Y., et al., 2016. Evaluation of precipitation input for SWAT modeling in Alpine catchment: a case study in the Adige river basin (Italy). Science of the Total Environment, 573, 66–82. doi:10.1016/j.scitotenv.2016.08.034
  • Ushio, T., et al., 2009. A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan. Ser. II, 87A, 137–151. doi:10.2151/jmsj.87A.137
  • van Diepen, M. and Franses, P.H., 2006. Evaluating Chi-squared automatic interaction detection. Information Systems, 31 (8), 814–831. doi:10.1016/j.is.2005.03.002
  • Wang, W.C., et al., 2015. Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources Management, 29 (8), 2655–2675. doi:10.1007/s11269-015-0962-6
  • Wang, Z., et al., 2020. Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling. Engineering Applications of Computational Fluid Mechanics, 14 (1), 1351–1372. doi:10.1080/19942060.2020.1830858
  • Xie, T., et al., 2019. Hybrid forecasting model for non-stationary daily runoff series: a case study in the Han River Basin, China. Journal of Hydrology, 577, 123915. doi:10.1016/j.jhydrol.2019.123915
  • Yaseen, Z.M., et al., 2018. Complementary data-intelligence model for river flow simulation. Journal of Hydrology, 567, 180–190. doi:10.1016/j.jhydrol.2018.10.020
  • Yaseen, Z.M., et al., 2017. Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. Journal of Hydrology, 554, 263–276. doi:10.1016/j.jhydrol.2017.09.007
  • Yaseen, Z.M., et al., 2015. Artificial intelligence based models for streamflow forecasting: 2000–2015. Journal of Hydrology, 530, 829–844. doi:10.1016/j.jhydrol.2015.10.038
  • Yaseen, Z.M., et al., 2019a. Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region. IEEE Access, 7, 74471–74481. doi:10.1109/ACCESS.2019.2920916
  • Yaseen, Z.M., et al., 2020. Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms. Water Resources Management, 34 (3), 1075–1091. doi:10.1007/s11269-020-02484-w
  • Yaseen, Z.M., et al., 2019b. An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. doi:10.1016/j.jhydrol.2018.11.069
  • Yin, Z., et al., 2018. Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment. Stochastic Environmental Research and Risk Assessment, 32 (9), 2457–2476. doi:10.1007/s00477-018-1585-2
  • Zounemat-Kermani, M., et al., 2020a. Neurocomputing in surface water hydrology and hydraulics: a review of two decades retrospective, current status and future prospects. Journal of Hydrology, 588, 125085. doi:10.1016/j.jhydrol.2020.125085
  • Zounemat-Kermani, M., et al., 2020b. Ensemble data mining modeling in corrosion of concrete sewer: a comparative study of network-based (MLPNN & RBFNN) and tree-based (RF, CHAID, & CART) models. Advanced Engineering Informatics, 43, 101030. doi:10.1016/j.aei.2019.101030

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.