Time Series-Based Annual Rainfall Estimation for Aksaray Province
Abstract views: 4
/ 
PDF downloads: 5
Keywords:
Time Series Analysis, Aksaray Rainfall Forecast, Deep Learning Models, Climate ChangeAbstract
In this study, annual precipitation data in Aksaray province of Turkey were examined by time
series analysis for the period 1980-2023 and future precipitation estimates were made. In the study,
statistical models such as Auto.ARIMA, Holt-winters and TBATS were used together with deep learning
models such as NNTAR, MLP and ELM. The performances of the models were evaluated with MAPE
values for various test lengths. While the NNTAR model stood out as the most successful model with the
lowest error rate among deep learning methods, Auto.ARIMA and Holt-Winters provided low error rates
in certain cases among statistical models. The research provides important findings for the sustainable
management of water resources in semi-arid regions such as Aksaray.
Downloads
References
Anon, “Applied Time Series Analysis.,” Inf Sci (N Y), vol. 29, no. 1, 1983, doi: 10.2307/2981500.
G. Papacharalampous, H. Tyralis, and D. Koutsoyiannis, “Predictability of monthly temperature and precipitation using automatic time series forecasting methods,” Acta Geophysica, vol. 66, no. 4, pp. 807–831, 2018, doi: 10.1007/s11600-018-0120-7.
R. Niu, C. Guo, Y. Zhang, L. He, and Y. Mao, “Study of ionospheric TEC short-term forecast model based on combination method,” International Conference on Signal Processing Proceedings, ICSP, vol. 2015-Janua, no. October, pp. 2426–2430, 2014, doi: 10.1109/ICOSP.2014.7015430.
I. Bozkurt, Z. Pala, and T. Etem, “Measurement and evaluation of low frequency electromagnetic field in camera observation rooms,” in 2017 13th International Conference Perspective Technologies and Methods in MEMS Design, MEMSTECH 2017 - Proceedings, 2017. doi: 10.1109/MEMSTECH.2017.7937554.
T. Etem, Z. Pala, and I. Bozkurt, “Electromagnetic pollution measurement in the system rooms of a university,” in 2017 13th International Conference Perspective Technologies and Methods in MEMS Design, MEMSTECH 2017 - Proceedings, 2017. doi: 10.1109/MEMSTECH.2017.7937548.
Z. Pala, “Using Decomposition-based Approaches to Time Series Forecasting in R Environment,” International Conference on Data Science, Machine Learning and Statistics - 2019 (DMS-2019), vol. 1, no. 1, pp. 231–233, 2019.
Z. Pala and R. Atici, “Forecasting Sunspot Time Series Using Deep Learning Methods,” Sol Phys, vol. 294, no. 5, 2019, doi: 10.1007/s11207-019-1434-6.
Z. Pala, “Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models,” Energy, vol. 263, no. PC, pp. 1–21, 2023, doi: 10.1016/j.energy.2022.125826.
Z. Pala and İ. H. Ünlük, “Comparison of hybrid and non-hybrid models in short-term predictions on time series in the R development environment,” DÜMF Mühendislik Dergisi, vol. 2, pp. 199–204, 2022, doi: 10.24012/dumf.1079230.
Z. Pala, İ. H. Ünlük, and E. Yaldız, “Forecasting of electromagnetic radiation time series: An empirical comparative approach,” Appl Comput Electromagn Soc J, vol. 34, no. 8, pp. 1238–1241, 2019.
Z. Pala, “Using forecastHybrid Package to Ensemble Forecast Functions in the R,” International Conference on Data Science, Machine Learning and Statistics - 2019 (DMS-2019), vol. 1, no. 1, pp. 45–47, 2019.
Z. Pala, V. Yamli, and I. H. Ünlük, “Deep Learning researches in Turkey: An academic approach,” in 2017 13th International Conference Perspective Technologies and Methods in MEMS Design, MEMSTECH 2017 - Proceedings, 2017. doi: 10.1109/MEMSTECH.2017.7937546.
Z. Pala, I. Bozkurt, and T. Etem, “Estimation of low frequency electromagnetic values using machine learning,” in 2017 13th International Conference Perspective Technologies and Methods in MEMS Design, MEMSTECH 2017 - Proceedings, 2017. doi: 10.1109/MEMSTECH.2017.7937550.
Z. Pala, “Examining EMF Time Series Using Prediction Algorithms With R,” vol. 44, no. 2, pp. 223–227, 2021.
E. Yaldız and Z. Pala, “Time Series Analysis of Radiological Data of Outpatients and Inpatients in Emergency Department of Mus State Hospital,” International Conference on Data Science, Machine Learning and Statistics - 2019 (DMS-2019), pp. 234–236, 2019.
Z. Pala, R. Atıcı, and E. Yaldız, “Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models,” Wirel Pers Commun, vol. 130, no. 2, pp. 1479–1502, 2023, doi: 10.1007/s11277-023-10341-3.
H. Abbasimehr and R. Paki, “Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization,” Chaos Solitons Fractals, vol. 142, Jan. 2021, doi: 10.1016/j.chaos.2020.110511.
M. Akpinar and N. Yumusak, “Estimating household natural gas consumption with multiple regression: Effect of cycle,” 2013 International Conference on Electronics, Computer and Computation, ICECCO 2013, pp. 188–191, 2013, doi: 10.1109/ICECCO.2013.6718260.
E. Özgüner, O. B. Tör, and A. N. Güven, “Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 25, no. 6, pp. 4923–4935, 2017, doi: 10.3906/elk-1612-206.
P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003, doi: 10.1016/S0925-2312(01)00702-0.
Z. Pala and M. Şana, “Attackdet: Combining web data parsing and real-time analysis with machine learning,” Journal of Advances in Technology and Engineering Research, vol. 6, no. 1, pp. 37–45, 2020, doi: 10.20474/jater-6.1.4.
Z. Pala and O. Özkan, “Artificial Intelligence Helps Protect Smart Homes against Thieves,” DÜMF Mühendislik Dergisi, vol. 11, no. 3, pp. 945–952, 2020, doi: 10.24012/dumf.700311.
I. Tugal and F. Sevgin, “Analysis and forecastıng of temperature using time series forecasting methods A Case Study of Mus,” Thermal Science, vol. 27, no. 4, pp. 3081–3088, 2023, doi: 10.2298/TSCI2304081T.
Z. Pala and F. Şevgin, “Statistical modeling for long-term meteorological forecasting: a case study in Van Lake Basin,” Natural Hazards, 2024, doi: 10.1007/s11069-024-06747-2.
İ. Pala, Zeydin;Bozkurt, “A Historical Overview of Low-Carbon Energy Consumption in the USA, Russia, China, and Japan,” in 3rd International Conference on Scientific and Academic Research, 2023, pp. 988–992. doi: 10.59287/as-proceedings.830.
Z. Pala, “Evolution of Low-Carbon Energy Consumption in Türkiye, United Kingdom, and France: A Historical Perspective,” pp. 546–549, 2023, doi: 10.59287/as-proceedings.744.
