Relative Humidity Forecasting as an Influent Factor in Hydro-climatology | ||
| AUT Journal of Civil Engineering | ||
| دوره 10، شماره 4، 2026 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2026.23975.5909 | ||
| نویسندگان | ||
| Yaser Sabzevari* 1، 2؛ Saeid Eslamian1؛ Saeid Okhravi2 | ||
| 1Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, 8415683111, Iran | ||
| 2Institute of Hydrology, Slovak Academy of Sciences, Dúbravská cesta 9, 84104, Bratislava, Slovakia | ||
| چکیده | ||
| Climate change is one of the major environmental challenges of the present era. Among the key climatic parameters, atmospheric humidity plays a crucial role in defining weather conditions, agriculture, human health, and various economic activities. Therefore, This study explores the forecasting of relative humidity (RH) as a significant factor in hydro-climatology using the ARIMA(0,1,1) model. The research utilizes historical RH data from 1993 to 2022, coupled with the Mann-Kendall test for trend analysis and time series techniques, to evaluate both past and future RH trends. The results indicate that while RH experienced a decreasing trend over the past three decades, this trend was not statistically significant because of Z statistical equal= -0.01 that is fewer than 1.96 threshold. Further analysis using the ARIMA model forecasts a slight increase in RH in the coming years. These findings suggest that, despite the absence of significant past trends, RH may show modest upward changes, which could influence future hydro-climatic conditions. The study highlights the importance of incorporating RH forecasting into hydro-climatological models for more accurate predictions of climate behavior and water resource management. Additionally, the research underscores the utility of time series analysis in understanding long-term climate variables and their implications for environmental planning. | ||
| کلیدواژهها | ||
| Relative Humidity؛ Mann-Kendall؛ Time series؛ ARIMA | ||
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آمار تعداد مشاهده مقاله: 57 تعداد دریافت فایل اصل مقاله: 55 |
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