Measuring and restructuring the risk in forecasting drought classes: an application of weighted Markov chain based model for standardised precipitation evapotranspiration index (SPEI) at one-month time scale

References

• Ali, Z., Hussain, I., Faisal, M., Nazir, H. M., Hussain, T. and co-authors. 2017. Forecasting drought using multilayer perceptron artificial neural network model. Adv. Meteorol. 2017, 1–9. doi:10.1155/2017/5681308
   Google Scholar
• Ahmad, Z., Hafeez, M. and Ahmad, I. 2012. Hydrology of mountainous areas in the Upper Indus Basin, Northern Pakistan with the perspective of climate change. Environ. Monit. Assess. 184, 5255–5274. doi:10.1007/s10661-011-2337-7
   Google Scholar
• Ali, Z., Hussain, I., Faisal, M., Almanjahie, I. M., Ahmad, I. and co-authors. 2019. A probabilistic weighted joint aggregative drought index (PWJADI) criterion for drought monitoring systems. Tellus. Dyn. Meteorol. Oceanogr. 1–21.
   Google Scholar
• Ali, Z., Hussain, I., Faisal, M., Almanjahie, I. M., Ismail, M. and co-authors. 2018. A new weighting scheme in weighted Markov model for predicting the probability of drought episodes. Adv. Meteorol. 2018, 1–10. doi:10.1155/2018/8954656
   Google Scholar
• Ali, Z., Hussain, I., Faisal, M., Nazir, H. M., Abd-el Moemen, M. and co-authors. 2017. A novel multiscalar drought index for monitoring drought: the standardized precipitation temperature index. Water Resour. Manage. 31, 4957–4969. doi:10.1007/s11269-017-1788-1
   Google Scholar
• Archer, D. R. and Fowler, H. J. 2004. Spatial and temporal variations in precipitation in the Upper Indus Basin, global teleconnections and hydrological implications. Hydrol. Earth Syst. Sci. 8, 47–61. doi:10.5194/hess-8-47-2004
   Google Scholar
• Arnhold, E. 2014. Easyanova: analysis of variance and other important complementary analyzes. R package version, 4.
   Google Scholar
• Awan, S. A. 2002. The climate and flood risk potential of northern areas of Pakistan. Science Vision 7, 100–109.
   Google Scholar
• Bacanli, U. G., Firat, M. and Dikbas, F. 2009. Adaptive neuro-fuzzy inference system for drought forecasting. Stoch. Environ. Res. Risk Assess. 23, 1143–1154. doi:10.1007/s00477-008-0288-5
   Google Scholar
• Benoit, G. 2005. Weighted Markov chains and graphic state nodes for information retrieval. Proc. Am. Soc. Info. Sci. Tech. 39, 115–123. doi:10.1002/meet.1450390113
   Google Scholar
• Chen, J. and Yang, Y. 2012. SPI-based regional drought prediction using weighted Markov chain model. Res. J. Appl. Sci. Eng. Technol. 4, 4293–4298.
   Google Scholar
• Chiang, C. L. 1968. Introduction to Stochastic Processes in Biostatistics, Vol. 95. Wiley, New York.
   Google Scholar
• Crommelin, D. and Vanden-Eijnden, E. 2006. Fitting timeseries by continuous-time Markov chains: a quadratic programming approach. Comput. Phys. 217, 782–805. doi:10.1016/j.jcp.2006.01.045
   Google Scholar
• De-di, L. and Chen X.-H. 2006. Annual precipitation forecasting based on the weighted Markov chain in Beijing River Basin. Journal of China Hydrology 6, 006.
   Google Scholar
• Edwards, B., Gray, M. and Hunter, B. 2009. A sunburnt country: the economic and financial impact of drought on rural and regional families in Australia in an era of climate change. Aust J Labour Econ. 12, 109.
   Google Scholar
• Gong, Z., Chen, C. and Ge, X. 2014. Risk prediction of low temperature in Nanjing city based on grey weighted Markov model. Nat. Hazards 71, 1159–1180. doi:10.1007/s11069-013-0690-2
   Google Scholar
• Gui, Y. and Shao, J. 2017. Prediction of precipitation based on weighted Markov chain in Dangshan. In Proceedings of the International Conference on High Performance Compilation, Computing and Communications, ACM, 81–85.
   Google Scholar
• Haan, C. T. 1977. Statistical Methods in Hydrology. The Iowa State University Press, Iowa.
   Google Scholar
• Hargreaves, G. H. 1994. Defining and using reference evapotranspiration. J. Irrig. Drain. Eng. 120, 1132–1139. doi:10.1061/(ASCE)0733-9437(1994)120:6(1132)
   Google Scholar
• Kaliakatsos-Papakostas, M. A., Epitropakis, M. G. and Vrahatis, M. N. 2011. Weighted Markov chain model for musical composer identification. In European Conference on the Applications of Evolutionary Computation, Springer, 334–343.
   Google Scholar
• Lange, K. 2010. Numerical Analysis for Statisticians. Springer, Berlin.
   Google Scholar
• Lee, H.-Y. and Chen, S.-L. 2006. Why use Markov-switching models in exchange rate prediction? Econ. Model. 23, 662–668. doi:10.1016/j.econmod.2006.03.007
   Google Scholar
• Le-Tian, X. 2005. Prediction of plum rain intensity based on index weighted Markov chain. J. Hydraul. Eng. 8, 988–993.
   Google Scholar
• Lohani, V. K. and Loganathan, G. 1997. An early warning system for drought management using the palmer drought index1. JAWRA Journal of the American Water Resources Association, 33, 1375–1386.
   Google Scholar
• Marden, J. I. 1996. Analyzing and Modeling Rank Data. CRC Press, Boca Raton.
   Google Scholar
• McKee, T. B., Doesken, N. J. and Kleist, J. 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Vol. 17, American Meteorological Society Boston, MA, pp. 179–183.
   Google Scholar
• Paulo, A. A. and Pereira, L. S. 2007. Prediction of SPI drought class transitions using Markov chains. Water Resour. Manage. 21, 1813–1827. doi:10.1007/s11269-006-9129-9
   Google Scholar
• Peng, Z., Bao, C., Zhao, Y., Yi, H., Xia, L. and co-authors. 2010. Weighted Markov chains for forecasting and analysis in incidence of infectious diseases in Jiangsu Province. J. Biomed. Res. 24, 207–214. doi:10.1016/S1674-8301(10)60030-9
   Google Scholar
• Sen, Z. 1990. Critical drought analysis by second-order Markov chain. J. Hydrol. (Amsterdam) 120, 183–202. doi:10.1016/0022-1694(90)90149-R
   Google Scholar
• Shatanawi, K., Rahbeh, M. and Shatanawi, M. 2013. Characterizing, monitoring and forecasting of drought in Jordan River Basin. JWARP. 05, 1192–1202. doi:10.4236/jwarp.2013.512127
   Google Scholar
• Spedicato, G. A., Kang, T. S., Yalamanchi, S. B. and Yadav, D. 2016. The Markovchain package: package for easily handling discrete Markov chains in r. Accessed Dec.
   Google Scholar
• Svoboda, M. and Fuchs, B. 2016. Handbook of Drought Indicators and Indices. Integrated Drought Management Tools and Guidelines Series. World Meteorological Organization, National Drought Mitigation Center, University of Nebraska-Lincoln, Nebraska-Lincoln.
   Google Scholar
• Takahashi, K., Morikawa, K., Takeda, D., Mizuno, A. 2007. Inventory control for a Markovian remanufacturing system with stochastic decomposition process. Int. J. Prod. Econ. 108, 416–425. doi:10.1016/j.ijpe.2006.12.023
   Google Scholar
• Thornthwaite, C. W. 1948. An approach toward a rational classification of climate. Geogr. Rev. 38, 55–94. doi:10.2307/210739
   Google Scholar
• Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim. 23, 1696–1718. doi:10.1175/2009JCLI2909.1
   Google Scholar
• Vicente-Serrano, S. M., Lo'pez-Moreno, J. I. and Van der Hurk, B. 2005. Hydrological response to different time scales of climatological drought: an evaluation of the standardized precipitation index in a mountainous Mediterranean basin. Hydrol. Earth Syst. Sci. 9, 523–533.
   Google Scholar
• White, G. F. 1974. Natural Hazards, Local, National, Global. Oxford University Press, Oxford.
   Google Scholar
• Zhou, P., Zhou, Y., Jin, J., Liu, L., Wang, Z. and co-authors. 2011. An improved Markov chain model based on autocorrelation and entropy techniques and its application to state prediction of water resources. Chin. Geogr. Sci. 21, 176–184. doi:10.1007/s11769-011-0447-3
   Google Scholar