The Use of Time Series and Artificial Neural Networks in Drought Simulation (Case Study: Bojnourd City)

Amiri, A., Baharvand, S., Rad, M. (2024). Evaluation of different drought indices to fnd the most Applicable Index (Case Study: Aleshtar Plain). Journal of Drought and Climate change Research, 1(4), 71-86. [In Persian]. Doi: 10.22077/jdcr.2023.6775.1043. 

Bazarafshan, J. (2008). Comparative study of some meteorological drought indicators in some climatic samples of Iran. Master’s thesis, Faculty of Natural Resources, University of Tehran. [In Persian].

Box, G. E., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society Series B: Statistical Methodology, 26(2), 211-243.

Dastorani, M. T., Afkhami, H., Maleki Nejad, H., & Mobin, M. H. (2009). Studying the application of artifcial neural network models in drought simulation and forecasting, the second national conference on the effects of drought and its management strategies, Isfahan. [In Persian]. https://civilica.com/doc/67349.

Deh Bozorgi, M. R.,; Malekian, A., & Ehsani, A. H. (2014). Evaluation of the performance of artifcial neural network in forecasting meteorological drought in northwest Iran, Applied Research Journal of Geographical Sciences, 15(36), 139-156. Doi: 20.1001.1.22287736.1394.15.36.8.4

Dehghani, A. A., & Ahmadi, R. (2008). Watershed estimation of watersheds without statistics using artifcial neural network, the frst international water conference, Zabul University. [In Persian]. https://civilica.com/doc/64342. 

Dikshit, A., Pradhan, B., & Santosh, M. (2022). Artifcial neural networks in drought prediction in the 21st century–A scientometric analysis, Applied Soft Computing, 114, 108080. Doi: https://doi.org/10.1016/j. asoc.2021.108080.

Ensaf Moghadam, T. (2007). Evaluation of several indicators of climatic drought and determination of the most appropriate index in the Salt Lake basin. Iranian Journal of Range and Desert Research. 14(2), 271-288. [In Persian]. https://ijrdr.areeo.ac.ir/article_105638.html. 

Evkaya, Ozan & Kurnaz, F. S. (2020). Forecasting drought using neural network approaches with transformed time series data. Journal of Applied Statistics. 48, 1-16. Doi: 10.1080/02664763.2020.1867829.

Esfandiyari, M., Malekinejad, H., Hakimzadeh, M. A., & Afkhami, H. (2016). Application of artifcial neural network model in drought prediction of Isfahan province, Watershed Promotion and Development Journal, 5(16), 25-34. [In Persian]. https://www.wmji. ir/article_696843_1091f8df7dc97 eb66b-066cabd459629a.pdf.

Fan, G., Sarabandi, A., & Yaghoobzadeh, M. (2021). Evaluating the climate change effects on temperature, precipitation and evapotranspiration in eastern Iran using CMPI5, Water Supply, 21 (8), 4316-4327. Doi: https://doi.org/10.2166/ws.2021.179

Golabi, M., Radmanesh, F. & Akhund Ali, A. M. (2013). Investigation of the performance of artifcial neural network and time series in modeling standard rainfall drought index (case study: selected stations of Khuzestan province), Journal of Arid Biome, 3(1), 82-87. [In Persian].

Helmi, M., & Shahidi, A. (2023). The using of SPI and SPEI indices in evaluating the effect of drought on quality of surface water resources (Case study: Kashafroud river). Journal of Drought and Climate change Research, 1(1), 83-96. [In Persian]. Doi: 10.22077/jdcr.2023.6023.1008. 

Jahangiri, M. H ,.Khoshmashraban, M., & Yousef., H. (2014). Monitoring and forecasting the drought condition using Standard Rainfall Index (SPI) and Multilayer Perceptron Neural Network (Case Study: Tehran and Alborz Provinces.) Ecohydrology Journal, 2(4), 417-428. [In Persian]. Doi:10.22059/ije.2015.58068. 

Kamali, Gh., & Nikzad, M,. (1999), Meteorological indicators in agricultural drought, Nivar Journal, 9-19. [In Persian].

Khoshgoftar, M. M., Akhundzadeh Hanzaei, M., & Khosravi, I. (2018). Comparison of ARIMA and neural network methods in drought modeling and monitoring using remote sensing time series data (case study: Arak city), Sepehr Geographical Information Journal, 109. [In Persian]. Doi: https:// doi.org/10.22131/sepehr.2019.35646. 

Kia, M. (2007), Neural Network in Matlab, Publications of Kian Sabz Computer Publishing Cooperative, Persian Gulf, Zanis. ISBN: 978-600-6021-41-6.

Menhaj, M. B. (2007). Basics of Neural Networks - Computational Intelligence, Volume I, Fifth Edition, Amirkabir University of Technology Publications. ISBN: 978-964-463-087-3

Meng, D., Shengzhi, H., Qiang, H., Guoyong, L., Yi Guo, L.,Wang, W.F., P, Li,. & Xudong Z. (2020). Assessing agricultural drought risk and its dynamic evolution characteristics, Agricultural Water Management10600 ,231 ,. https://doi. org/10.1016/j.agwat.2020.106003.

Mohamadyariyan, M., Mofdi, A., & Mokaram, L. (2013). Using the SPI index in zoning droughts and disasters Northeast Iran with AHP method In the G environment, National conference „Challenges of adaptation to climate change and solutions to reduce its adverse effects, North University.

Mirdar Soltani, A. H., Heydari, Kh., Safar & Alizadeh, A. H. (2019). The use of neural network and regression methods in rainfall time series modeling, the 6th International Conference on Mechanics, Construction, Industries and Civil Engineering. [In Persian].

Mishra, N., Soni, H., Sharma, S., & Upadhyay, A. (2018). Development and Analysis of Artifcial Neural Network Models for Rainfall Prediction by Using Time-Series Data. International Journal of Intelligent Systems and Applications. Doi: 10. 16-23. 10.5815/ijisa.2018.01.03.

Mishra, A.K., & Desai, V.R. (2005). Drought Forecasting Using Stochastic Models, Stochastic Environmental Research and Risk Assessment (SERRA), 19(5), 326-339. Doi: 10.1007/s00477-005-0238-4.

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Preprints, Eighth Conference on Applied Climatology. Anaheim, California.

Najaf, M. S., Qaini Hesarouieh, M., & Fedai Kermani, E. (2021). Drought prediction using artifcial neural network and SPI index, 8th National Conference on Sustainable Development in Civil Engineering, Tehran, [In Persian]. https://civilica.com/doc/1240078. 

Sadeghiyan, M., Kerami, H., & Mousavi, S. F. (2019). Performance evaluation of time series models, neural network and neural-fuzzy inference system in meteorological drought forecasting (case study: Semnan synoptic station), Journal of Irrigation Engineering Sciences, 43(2), 1-18. [InPersian]. Doi: 10.22055/jise.2017.17729.1283. 

Sharifpour, L., Qanei Bafghi, M. J., Kothari, M. R., & Sharifpour, S. (2021). Comparing the efciency of four artifcial intelligence methods in drought prediction, Journal of Spatial Analysis of Environmental Hazards, 8th year, 30, 139-156. [In Persian]. Doi: 10.52547/jsaeh.8.3.139. 

Salehi., H. (2016). Presenting an algorithm to achieve the uncertainty of the artificial neural network model in drought forecasting, master’s thesis, Shahrood University of Technology. [In Persian].

Sayadi, O. (2007). Introduction to Artifcial Neural Networks, Laboratory of Vital Signals and Medical Images Processing, Faculty of Electrical Engineering, Sharif University of Technology. [In Persian].

Wabel, P., Jha, M. K., Adamala, S., Tiwari, M. K., & Biswal, S. (2023). Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India. Environmental monitoring and assessment. 24,195 (9). Doi: 10.1007/s10661-023-11631-w. PMID: 37615733.

Yonesi, M., Shahraki, N., Marof, S., & Nozari, H. (2017). Drought forecasting using the integrated model of artifcial neural network-wavelet and ARIMA time series model, Journal of Irrigation Science and Engineering, 41(2), 167-181. [In Persian]. Doi: 20.1001.1.25885952.1397.41.2.12.0.