Pre-processing satellite rainfall products improves hydrological simulations with machine learning

References

• Agarwal, A. and Singh, R.D., 2004. Runoff modelling through back propagation artificial neural network with variable rainfall-runoff data. Water Resources Management, 18 (3), 285–300.
   Web of Science ®Google Scholar
• Alizadeh, M.J., et al., 2017. A new approach for simulating and forecasting the rainfall-runoff process within the next two months. Journal of Hydrology, 548, 588–597.
   Web of Science ®Google Scholar
• Arthur, D. and Vassilvitskii, S., 2007. K-means++: the advantages of careful seeding. Proceedings of the Annuual ACM-SIAM Symposium on Discrete Algorithms, 8, 1027–1035.
   Google Scholar
• Asong, Z.E., et al., 2017. Evaluation of integrated multisatellite retrievals for GPM (IMERG) over southern Canada against ground precipitation observations: a preliminary assessment. Journal of Hydrometeorology, 18 (4), 1033–1050.
   Web of Science ®Google Scholar
• Behzad, M., et al., 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications, 36 (4), 7624–7629.
   Web of Science ®Google Scholar
• Capó, M., et al., 2017. An efficient approximation to the K-means clustering for massive data. Knowledge-Based Systems, 117, 56–69.
   Web of Science ®Google Scholar
• Chen, S., et al., 2013. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China. Journal of Geophysical Research: Atmospheres, 118 (23), 13–060.
   Web of Science ®Google Scholar
• Dezfuli, A.K., et al., 2017. Validation of IMERG precipitation in Africa. Journal of Hydrometeorology, 18 (10), 2817–2825.
   PubMed Web of Science ®Google Scholar
• Fa, W., et al., 2011. Modeling polarimetric radar scattering from the lunar surface: study on the effect of physical properties of the regolith layer. Journal of Geophysical Research: Planets, 116 (E3).
   Google Scholar
• Farmani, R., et al., 2017. Pipe failure prediction in water distribution systems considering static and dynamic factors. Procedia Engineering, 186, 117–126.
   Google Scholar
• Fatichi, S., et al., 2016. An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology, 537, 45–60.
   Web of Science ®Google Scholar
• Feki, H., et al., 2018. Characterisation of Mean Monthly Rainfall Variability Over Mellegue Catchment—Tunisia. In: A. Kallel, M. Ksibi, H. Ben Dhia, and N. Khélifi, eds. Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions. EMCEI 2017. Advances in Science, Technology & Innovation. Springer, Cham.
   Google Scholar
• Gebremichael, M. and Hossain, F., 2010. Satellite rainfall applications for surface hydrology. Berlin: Springer, 327.
   Google Scholar
• Guermazi, E., Milano, M., and Reynard, E., 2019. Performance evaluation of satellite-based rainfall products on hydrological modeling for a transboundary catchment in northwest Africa. Theor. Appl. Climatol, 138, 1695–1713.
   Web of Science ®Google Scholar
• Guermoui, M., et al., 2021. Forecasting intra-hour variance of photovoltaic power using a new integrated model. Energy Conversion and Management, 245, 114569.
   Web of Science ®Google Scholar
• Gupta, H.V., et al., 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4 (2), 135–143.
   Web of Science ®Google Scholar
• Habib, E.H. and Nasrollahi, N., 2009. Evaluation of TRMM-TMPA satellite rainfall estimates over arid regions. American Geophysical Union, Fall Meeting 2009, 2009: H12A–02.
   Google Scholar
• Haghjouei, H., et al., 2022. Experimental study demonstrating a cost-effective approach for generating 3D-enhanced models of sediment flushing cones using model-based SFM photogrammetry. Water, 14 (10), 1588.
   Web of Science ®Google Scholar
• Hou, A.Y., et al., 2014. The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95 (5), 701–722.
   Web of Science ®Google Scholar
• Huang, G.-B., et al., 2006. Extreme learning machine: theory and applications. Neurocomputing, 70 (1–3), 489–501.
   Web of Science ®Google Scholar
• Jiang, L. and Bauer-Gottwein, P., 2019. How do GPM IMERG precipitation estimates perform as hydrological model forcing? Evaluation for 300 catchments across Mainland China. Journal of Hydrology, 572, 486–500.
   Web of Science ®Google Scholar
• Juan, C., et al., 2017. ANN model-based simulation of the runoff variation in response to climate change on the Qinghai-Tibet plateau, China. Advances in Meteorology, 2017, 9451802, 13.
   Web of Science ®Google Scholar
• Kashani, M.H., et al., 2016. Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran. Journal of Hydrology, 540, 340–354.
   Web of Science ®Google Scholar
• Kratzert, F. et al., 2021. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling. Hydrology and Earth System Sciences, 25, 2685–2703.
   Web of Science ®Google Scholar
• Likas, A., et al., 2003. The global k-means clustering algorithm. Pattern Recognition, 36 (2), 451–461.
   Web of Science ®Google Scholar
• Maggioni, V., et al., 2016. A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era. Journal of Hydrometeorology, 17 (4), 1101–1117.
   Web of Science ®Google Scholar
• McCuen Richard, H., et al., 2006. Evaluation of the Nash–Sutcliffe efficiency index. Journal of Hydrologic Engineering, 11 (6), 597–602.
   Web of Science ®Google Scholar
• Milewski, A., et al., 2015. Assessment and comparison of TMPA satellite precipitation products in varying climatic and topographic regimes in Morocco. Remote Sensing, 7 (5), 5697–5717.
   Web of Science ®Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I—a discussion of principles. Journal of Hydrology, 10 (3), 282–290.
   Google Scholar
• Nearing, G.S., et al., 2021. What role does hydrological science play in the age of machine learning? Water Resources Research, 57 (3), e2020WR028091.
   Web of Science ®Google Scholar
• Nourani, V., et al., 2013. Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. Journal of Hydrology, 476, 228–243.
   Web of Science ®Google Scholar
• Nourani, V., et al., 2021. Ensemble data-driven rainfall-runoff modeling using multi-source satellite and gauge rainfall data input fusion. Earth Science Informatics, 14 (4), 1787–1808.
   Web of Science ®Google Scholar
• Pakoksung, K. and Takagi, M., 2016. Effect of satellite based rainfall products on river basin responses of runoff simulation on flood event. Modeling Earth Systems and Environment, 2 (3), 143.
   Google Scholar
• Panthong, R. and Srivihok, A., 2015. Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Procedia Computer Science, 72, 162–169.
   Google Scholar
• Prasad, R., et al., 2017. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm. Atmospheric Research, 197, 42–63.
   Web of Science ®Google Scholar
• Qin, Y., et al., 2014. Evaluation of satellite rainfall estimates over the Chinese Mainland. Remote Sensing, 6 (11), 11649–11672.
   Web of Science ®Google Scholar
• Ritter, A. and Muñoz-Carpena, R., 2013. Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. Journal of Hydrology, 480, 33–45.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Sharif, H. and Al-Zahrani, M., 2017. Urban flood modeling driven by IMERG satellite products. Geophysical Research Abstracts, 16, EGU2017–17448.
   Google Scholar
• Solomatine, D.P. and Ostfeld, A., 2008. Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics, 10 (1), 3–22.
   Web of Science ®Google Scholar
• Tang, G., et al., 2016. Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. Journal of Hydrology, 533, 152–167.
   Web of Science ®Google Scholar
• Tang, G., et al., 2020. Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sensing of Environment, 240, 111697.
   Web of Science ®Google Scholar
• Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106 (D7), 7183–7192.
   Web of Science ®Google Scholar
• Uysal, G., et al., 2016. Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products. Journal of Hydrology, 543, 630–650.
   Web of Science ®Google Scholar
• Wang, C., et al., 2018. Global intercomparison and regional evaluation of GPM IMERG Version-03, Version-04 and its latest Version-05 precipitation products: similarity, difference and improvements. Journal of Hydrology, 564, 342–356.
   Web of Science ®Google Scholar
• Wen, X., et al., 2019. Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems. Journal of Hydrology, 570, 167–184.
   Web of Science ®Google Scholar
• Young, C.-C., et al., 2017. A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Applied Soft Computing, 53, 205–216.
   Web of Science ®Google Scholar
• Yuan, F., et al., 2017. Assessment of GPM and TRMM multi-satellite precipitation products in streamflow simulations in a data-sparse mountainous watershed in Myanmar. Remote Sensing, 9 (3), 302.
   Web of Science ®Google Scholar