Document Type : Research Article

Authors

Department of Renewable Energies and Environment, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.

Abstract

Drought is considered as a destructive disaster that can have irreversible effects on different aspects of life. In this study, artificial neural network was used as a powerful means of modeling nonlinear and indefinite processes in order to simulate drought intensities at 7 synoptic stations of Khorasan Razavi from more than 35 years ago up to the year 2014. Input data were the calculations of the two indicators of PNPI and SPI by DIC software, and the output layer (drought intensity) was taken to the Matlab software and employed as the teaching data (from 25 years), experiment (from 5 years), and validation (from another 5 years). The 3-9-1 structure of the network of layers had the maximum accuracy with the error rate of less than 2 % and high correlation (more than 90 %). After trial and error for each station through sigmoid stimulation function in the Perceptron network, it was observed that the stations of Mashhad and Quchan had the minimum error and the maximum error was related to the station of Neyshabur. The results of comparisons and observations showed that the artificial neural network had high efficiency in simulation of the data. The obtained correlation amount of 0.999 for the base station represented the small error of the model in prediction. Drought forecasting was performed in this study by the trained algorithm in the artificial neural network without using the observation data. The results showed that rainfall, temperature, and speed models had a positive role in forecasting the provinces that would experience drought. Due to its lower amount of error, SPI indicator was selected for mapping, the findings of which showed that the highest drought intensity belonged to the near normal to normal wet lands.

Keywords

Main Subjects

1.     Zambrano, F., Vrieling, A., Nelson, A., Meroni, M. and Tadesse, T., "Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices", Remote Sensing of Environment, Vol. 219, (2018), 15-30. (https://doi.org/10.1016/j.rse.2018.10.006).
2.     Hao, Z., Singh, V.P. and Xia, Y., "Seasonal drought prediction: Advances, challenges, and future prospects", Reviews of Geophysics, Vol. 56, No. 1, (2018), 108-141. (https://doi: 10.1002/2016RG000549).
3.     Wang, W., Chau, K., Xu, D., Qiu, L. and Liu, C., "The annual maximum flood peak discharge forecasting using Hermite projection pursuit regression with SSO and LS method", Water Resources Management, Vol. 31, No. 1, (2017), 461-477. (https://doi: 10.1007/s11269-016-1538-9).
4.     Miralles, D.G., Gentine, P., Seneviratne, S.I. and Teuling, A.J., "Land–atmospheric feedbacks during droughts and heatwaves: State of the science and current challenges", Annals of the New York Academy of Sciences, Vol. 1436, No. 1, (2019), 19. (https://doi: 10.1111/nyas.13912).
5.     Kim, T.-W. and Valdés, J.B., "Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks", Journal of Hydrologic Engineering, Vol. 8, No. 6, (2003), 319-328. (https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(319).
6.     Deka, P. and Chandramouli, V., "Fuzzy neural network model for hydrologic flow routing", Journal of Hydrologic Engineering, Vol. 10, No. 4, (2005), 302-314. (https://doi.org/10.1061/(ASCE)1084-0699(2005)10:4(302).
7.     Ardabili, S., Mosavi, A., Dehghani, M. and Várkonyi-Kóczy, A.R., "Deep learning and machine learning in hydrological processes climate change and earth systems: A systematic review",Lecture Notes in Networks and Systems, Vol. 101, (2020), 52-62. (https://doi: 10.1007/978-3-030-36841-8_5).
8.     Pashazadeh, A. and Javan, M., "Comparison of the gene expression programming, artificial neural network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers", Theoretical and Applied Climatology, Vol. 139, No. 3–4, (2020), 1349-1362. (https://dio: 10.1007/s00704-019-03032-2).
9.     Tokar, A.S. and Markus, M., "Precipitation-runoff modeling using artificial neural networks and conceptual models", Journal of Hydrologic Engineering, Vol. 5, No. 2, (2000), 156-161. (https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(156)).
10.   Singh, P. and Deo, M.C., "Suitability of different neural networks in daily flow forecasting", Applied Soft Computing, Vol. 7, No. 3, (2007), 968-978. (https://doi.org/10.1016/j.asoc.2006.05.003).
11.   Wang, W., Van Gelder, P.H., Vrijling, J.K. and Ma, J., "Forecasting daily streamflow using hybrid ANN models", Journal of Hydrology, Vol. 324, No. 1-4, (2006), 383-399. (https://doi.org/10.1016/j.jhydrol.2005.09.032).
12.   Mishra, A.K. and Desai, V.R., "Drought forecasting using feed-forward recursive neural network", Ecological Modelling, Vol. 198, No. 1–2, (2006), 127-138. (https://doi.org/10.1016/j.ecolmodel.2006.04.017).
13.   Sedaghatkerdar, A. and Fattahi, E., "Indicators of drought in Iran", Journal of Geography and Development, Vol. 11, (2008), 59-76.
14.   Afkhami, H., Dastorani, M.T., Malekinejad, H. and Mobin, M.H., "The effect of climatic factors on the accuracy of artificial neural network based drought prediction in Yazd region", Proceedings of 5th National Conference on Watershed Management Science and Engineering of Iran, Iranian Watershed Association, Retrieved from (2009), 1-12. (https://www.civilica.com/Paper-WATERSHED05-WATERSHED05_278.html)
15.   Farahmand, F., Galka, F. and Farahmand, A., "Evaluation of artificial neural networks performance in modeling river water contamination using qualitative parameters", First National Conference on New Technologies in Engineering Sciences, Islamic, (2009).
16.   Dastjerdi, J. and Hoseini, S.M., "Application of artificial neural network in climate element simulation and drought cycle forecasting case study: Isfahan province", Geography and Environmental Planning, Retrieved from (2010), Vol. 21, No. 3, 107-120. (http://ensani.ir/fa/article/231193).
17.   Nasri, M., "Application of artificial neural networks (ANNs) in prediction models in risk management", World Applied Sciences Journal, Vol. 10, No. 12, (2010), 1493-1500. (https://doi.org/10.1.1.390.3895).
18.   Rezaeian-Zadeh, M. and Tabari, H., "MLP-based drought forecasting in different climatic regions", Theoretical and Applied Climatology, Vol. 109, No. 3–4, (2012), 407-414. (https://doi.org/10.1007/s00704-012-0592-3).
19.   Afkhami, H., Dastorani, M.T., Malekinejad, H. and Mobin, M.H., "Effects of climatic factors on accuracy of ANN-based drought prediction in Yazd area", TT. JSTNAR, Retrieved from http://jstnar.iut.ac.ir/article-1-1215-fa.html, (2010). (http://jstnar.iut.ac.ir/article-1-1215-fa.html).
20.   Mousavi, B.M. and Ashrafi, B., "The study of synoptic patterns that caused autumn and winter droughts in Khorasan Razavi Province", Journal of Soil and Water Conservation, Vol. 4, No. 18, (2011), 167-184.
21.   Bai, L., David, R. and Hernick, M., "Apparatus and method for controlling a fuel cell using the rate of voltage recovery", U.S. Patent 7,722,972, B2 Appl. NO.: 11/207,123, (2010).
22.   Schalkoff, R.J., Artificial neural networks, McGraw-Hill Higher Education, (1997). (https://doi/10.5555/541158).
23.   Maniezzo, V., "Genetic evolution of the topology and weight distribution of neural networks", IEEE Transactions on Neural Networks, Vol. 5, No. 1, (1994), 39-53. (https://doi/10.1109/72.265959).