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Articles

Predicting Standardized Streamflow index for hydrological drought using machine learning models

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Pages 339-350 | Received 14 Oct 2019, Accepted 05 Jan 2020, Published online: 29 Jan 2020

References

  • Adamowski, J., & Prasher, S. O. (2012). Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data. Journal of Water and Land Development, 17(1), 89–97.
  • Adarsh, S., & Janga Reddy, M. (2019). Evaluation of trends and predictability of short-term droughts in three meteorological subdivisions of India using multivariate EMD-based hybrid modelling. Hydrological Processes, 33(1), 130–143.
  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration- Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome. 300(9).
  • Basak, D., Pal, S., & Patranabis, D. C. (2007). Support vector regression. Neural Information Processing, 11, 203–225.
  • Bhattacharya, B., & Solomatine, D. P. (2005). Neural networks and M5 model trees in modeling water level-discharge relationship for an Indian river. Neurocomputing, 63, 381–396.
  • Bhattacharya, B., & Solomatine, D. P. (2006). Machine learning in sedimentation modeling. Neural Networks, 19(2), 208–214.
  • Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classiers. In D. Haussler (Ed.), 5th annual ACM Workshop on COLT (pp. 144–152). Pittsburgh, PA: Wiley.
  • Choubin, B., Abdolshahnejad, M., Moradi, E., Querol, X., Mosavi, A., Shamshirband, S., & Ghamisi, P. (2020). Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain. Science of The Total Environment, 701, 134474.
  • Choubin, B., Malekian, A., & Golshan, M. (2016). Application of several data-driven techniques to predict a standardized precipitation index. Atmósfera, 29(2), 121–128.
  • Deo, R. C., Ghorbani, M. A., Samadianfard, S., Maraseni, T., Bilgili, M., & Biazar, M. (2018). Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renewable Energy, 116, 309–323.
  • Deo, R. C., Kisi, O., & Singh, V. P. (2017). Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmospheric Research, 184, 149–175.
  • Faroughi, M., Karimimoshaver, M., Aram, F., Solgi, E., Mosavi, A., Nabipour, N., & Chau, K. W. (2020). Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship. Engineering Applications of Computational Fluid Mechanics, 14(1), 254–270.
  • Ferreira, C. (2001a). Gene expression programming in problem solving. 6th Online World Conf. on Soft Computing in Industrial Applications (invited Tutorial).
  • Ferreira, C. (2001b). Gene expression programming, A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87.
  • Fung, K. F., Huang, Y. F., & Koo, C. H. (2018). Improvement of SVR-Based Drought Forecasting Models using Wavelet Pre-Processing Technique. In E3S Web of Conferences (Vol. 65, p. 07007). EDP Sciences.
  • Hauduc, H., Neumann, M. B., Muschalla, D., Gamerith, V., Gillot, S., & Vanrolleghem, P. A. (2015). Efficiency criteria for environmental model quality assessment: A review and its application to wastewater treatment. Environmental Modelling & Software, 68, 196–204.
  • Heim Jr., R. R. (2002). A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(8), 1149–1166.
  • Hemmati-Sarapardeh, A., Hajirezaie, S., Soltanian, M. R., Mosavi, A., Nabipour, N., Shamshirband, S., & Chau, K. W. (2020). Modeling natural gas compressibility factor using a hybrid group method of data handling. Engineering Applications of Computational Fluid Mechanics, 14(1), 27–37.
  • Kurup, P. U., & Dudani, N. K. (2014). Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering, 128, 569–579.
  • Mckee, T. B., Doesken, N. J., & Leist, J. (1993). The relationship of drought frequency and duration to time scales. Preprints 8th Conference on Applied Climatology, 17, 179–184.
  • Mishra, A. K., & Singh, V. P. (2011). Drought modeling - a review. Journal of Hydrology, 403(1-2), 157–175.
  • Mouatadid, S., Raj, N., Deo, R. C., & Adamowski, F. (2018). Input selection and data-driven model performance optimization to predict the standardized precipitation and evaporation index in a drought-prone region. Atmospheric Research, 212, 130–149.
  • Nabipour, N. (2020). Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized with Artificial Neural Networks. IEEE Access, 155, 225–233. doi:10.1109/ACCESS.2020.2964584.
  • Palmer, W. C. (1965). Meteorological drought. US Department of Commerce. Washington, DC, USA: Weather Bureau.
  • Parasuraman, K., Elshorbagy, A., & Carey, S. K. (2007). Modelling the dynamics of the evapotranspiration process using genetic programming. Hydrological Sciences Journal, 52(3), 563–578.
  • Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187.
  • Quinlan, J. R. (1992). Learning with continuous classes. In Proc. AI’92 (Fifth Australian Joint Conference on Artificial Intelligence (pp. 343–348). Singapore: World Scientific.
  • Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., & Perera, B. J. C. (2018). Statistical downscaling of precipitation using machine learning techniques. Atmospheric Research, 212, 240–258.
  • Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019). Application of Support Vector Regression for modeling low flow time series. KSCE Journal of Civil Engineering, 14, 1–12.
  • Samadianfard, S., Ghorbani, M. A., & Mohammadi, B. (2018). Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled hybrid firefly optimizer algorithm. Information Processing in Agriculture, 5, 465–476.
  • Samadianfard, S., Jarhan, S., Salwana, E., Mosavi, A., Shamshirband, S., & Akib, S. (2019). Support vector regression integrated with fruit fly optimization algorithm for river flow forecasting in Lake Urmia Basin. Water, 11, 1934.
  • Samadianfard, S., Majnooni-Heris, A., Qasem, S. N., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate. Engineering Applications of Computational Fluid Mechanics, 13(1), 142–157.
  • Sattari, M. T., Farkhondeh, A., & Abraham, J. P. (2018). Estimation of sodium adsorption ratio indicator using data mining methods: A case study in Urmia Lake basin, Iran. Environmental Science and Pollution Research, 25(5), 4776–4786.
  • Sattari, M. T., Pal, M., Apaydin, H., & Ozturk, F. (2013). M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resources, 40(3), 233–242.
  • Shabani, S., Samadianfard, S., Sattari, M. T., Mosavi, A., Shamshirband, S., Kmet, T., & Várkonyi-Kóczy, A. R. (2020). Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and Support Vector machines; comparative analysis. Atmosphere, 11(1), 66.
  • Sheffield, J., Wood, E. F., & Roderick, M. L. (2012). Little change in global drought over the past 60 years. Nature, 491(7424), 435–438.
  • Shukla, S. H., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2), 41–46.
  • Soh, Y. W., Koo, C. H., Huang, Y. F., & Fung, K. F. (2018). Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia. Computers and Electronics in Agriculture, 144, 164–173.
  • Sumi, S. M., Zaman, M., & Hirose, H. (2012). A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. International Journal of Applied Mathematics and Computer Science, 22(4), 841–854.
  • Tirivarombo, S., Osupile, D., & Eliasson, P. (2018). Drought monitoring and analysis: Standardised precipitation evapotranspiration index (SPEI) and standardised precipitation index (SPI). Physics and chemistry of the Earth. Parts A/B/C, 106, 1–10.
  • Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.
  • Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
  • Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718.
  • Wilhite, D. A. (2000). Drought: A global assessment. Volume I. London and New York: Rutledge Press.
  • Yin, Z., Feng, Q., Wen, X., Deo, R. C., Yang, L., Si, J., & He, Z. (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.
  • Zounemat-Kermani, M., Seo, Y., Kim, S., Ghorbani, M. A., Samadianfard, S., Naghshara, S., … Singh, V. P. (2019). Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida. Applied Science, 9, 2534.