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Forecasting and Modeling of Dew Point Temperature in Meteorological Stations of Eastern Region of Iran Based on VAR and VAR-GARCH Models | ||
| Water Harvesting Research | ||
| دوره 7، شماره 1، 2024، صفحه 34-50 اصل مقاله (2.07 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2024.7634.1138 | ||
| نویسندگان | ||
| Vahid Khorramnezhad1؛ Abolfazl akbarpour* 2؛ Mohammad Nazeri Tahroudi3 | ||
| 1MSc Student, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| 2Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| 3Assistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran. | ||
| چکیده | ||
| In this research, modeling and estimation of dew point temperature values in eight meteorological stations located in the eastern regions of Iran were done. These stations, including Bam, Birjand, Iranshahr, Kerman, Mashhad, Tabas, Zabol and Zahan, are all characterized by a dry climate. First, the correlation of different weather parameters with dew point temperature was investigated and then the parameters of mean temperature, maximum temperature and minimum temperature were selected as the parameters with the highest correlation to dew point temperature. These selected parameters then incorporated into a VAR (Vector Autoregression) model as inputs for estimating dew point temperature values. This modeling approach allows us to capture the interdependencies between these variables and enhance our accuracy in predicting dew point temperature. Then the stability of the residual series of the VAR model was investigated and the residual series of this model was developed using the generalized ARCH model. The result of the development of the VAR model was the investigation of the dew point temperature in eight meteorological stations with the VAR-GARCH model. The results indicated that this combined model outperformed VAR model in both the train and test phases. Specifically, the VAR-GARCH model demonstrated higher accuracy and improved results compared to solely using a VAR model. The incorporation of GARCH allowed better modeling of the residual series, leading to an overall increase in accuracy ranging from 5% to 30% during the test phase. These findings suggest that considering both autoregressive dynamics and conditional heteroskedasticity is crucial for accurately predicting dew point temperatures. By incorporating GARCH into our modeling approach, we were able to capture additional information about volatility and further enhance our predictions. | ||
| کلیدواژهها | ||
| VAR model؛ Climate؛ Dew point؛ Forecast؛ ARCH model | ||
| مراجع | ||
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Aguirre-Gutiérrez, C. A., Holwerda, F., Goldsmith, G. R., Delgado, J., Yepez, E., Carbajal, N., ... & Arredondo, J. T. (2019). The importance of dew in the water balance of a continental semiarid grassland. Journal of Arid Environments, 168, 26-35.
Baguskas, S. A., King, J. Y., Fischer, D. T., D’Antonio, C. M., & Still, C. J. (2016). Impact of fog drip versus fog immersion on the physiology of Bishop pine saplings. Functional plant biology, 44(3), 339-350.
Campbell, J. Y., Lo, A. W., MacKinlay, A. C., & Whitelaw, R. F. (1998). The econometrics of financial markets. Macroeconomic Dynamics, 2(4), 559-562.
Culbertson, K. (1996). Quantitative Financial Economics: Stocks, Bonds and Foreign Exchange. John Wiley and Sons, Chichester.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.
Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press, Princeton.
Khashei‐Siuki, A., Shahidi, A., Ramezani, Y., & Nazeri Tahroudi, M. (2021). Simulation of potential evapotranspiration values based on vine copula. Meteorological Applications, 28(5), e2027.
Lin, J., Shahzad, M. W., Li, J., Long, J., Li, C., & Chua, K. J. (2021). A robust physics-based model framework of the dew point evaporative cooler: From fundamentals to applications. Energy Conversion and Management, 233, 113925.
Lutkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin. 11.7 References 427
Lutkepohl, H. (1999). "Vector Autoregressions," unpublished manuscript, Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin.
Mills, T.C. (1999). The Econometric Modeling of Financial Time Series, Second Edition. Cambridge University Press, Cambridge.
Nazeri-Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2022). Bivariate simulation of potential evapotranspiration using copula-GARCH model. Water Resources Management, 36(3), 1007-1024.
Ramezani, Y., Nazeri Tahroudi, M., De Michele, C., & Mirabbasi, R. (2023). Application of copula-based and ARCH-based models in storm prediction. Theoretical and Applied Climatology, 151(3), 1239-1255.
Salas, J. D. (1980). Applied modeling of hydrologic time series. Water Resources Publication.
Shahidi, A., Ramezani, Y., Nazeri-Tahroudi, M., & Mohammadi, S. (2020). Application of vector autoregressive models to estimate pan evaporation values at the Salt Lake Basin, Iran. IDŐJÁRÁS/QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE, 124(4), 463-482.
SIMS, C. I. A. (1980). ECONO MI ETRICA. Econometrica, 48(1), 1-48.
Tomaszkiewicz, M., Abou Najm, M., Zurayk, R., & El-Fadel, M. (2017). Dew as an adaptation measure to meet water demand in agriculture and reforestation. Agricultural and Forest Meteorology, 232, 411-421.
Tsay, R. S. (2005). Analysis of financial time series. John wiley & sons.
Waggoner, D. F., & Zha, T. (1999). Conditional forecasts in dynamic multivariate models. Review of Economics and Statistics, 81(4), 639-651.
Watson, M. W. (1994). Vector autoregressions and cointegration. Handbook of econometrics, 4, 2843-2915. | ||
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