Climatology
Ali Zarei; Asadollah Khoorani
Abstract
The study aimed to introduce the most effective model for estimating energy consumption through the modeling of residential electrical energy consumption. .Reanalysis climate data from ECMWF spanning the years 2003 to 2022 were acquired, along with the annual electrical energy consumption data of the ...
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The study aimed to introduce the most effective model for estimating energy consumption through the modeling of residential electrical energy consumption. .Reanalysis climate data from ECMWF spanning the years 2003 to 2022 were acquired, along with the annual electrical energy consumption data of the residential sector across Iranian provinces. Pearson correlation coefficient was employed to analyze the relationships between variables, and the Mann-Kendall non-parametric test was utilized to scrutinize trends in these variables. Four regression and Artificial Intelligence (AI)-based models, namely Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF), were employed to model electric energy consumption. The performance of these models was assessed using metrics such as Root Mean Squared Error (RMSE), Coefficient of Determination (R2), and Standard Deviation (SD), evaluated through a Taylor diagram. Provinces. In 22 provinces, a positive correlation was observed, whereas in 9 provinces, a negative correlation was identified. Analysis of the temporal changes indicates a consistent increase in minimum and maximum temperatures as well as electrical energy consumption across all provinces. However, it is noteworthy that the UTCI displayed a negative trend in several provinces. The stepwise regression model revealed that in 23 provinces, the sole influential variable is the minimum temperature. Notably, in the provinces of Isfahan, South Khorasan, and Kerman, both minimum temperature and the UTCI were identified as influential variables. Conversely, in Ardabil, Gilan, and Golestan provinces, only the maximum temperature featured in the regression equation. Modeling outcomes underscored the superior performance of the ANN model in comparison to the other three models. The ANN model exhibited the highest correlation coefficient at 0.79, coupled with the RMSE of 360. Following in ranking, the MLR, SVM, and RF models demonstrated progressively lower levels of performance
Climatology
Ebrahim Mesgari; Taghi Tavousi; Peyman Mahmoudi
Abstract
Introduction Frost is one of the most important phenomena in climatology, which is caused by changes in temperature over time. The sudden occurrence of this phenomenon at the beginning and end of the cold period can be very dangerous for the agricultural sector. Therefore, the awareness of the frost ...
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Introduction Frost is one of the most important phenomena in climatology, which is caused by changes in temperature over time. The sudden occurrence of this phenomenon at the beginning and end of the cold period can be very dangerous for the agricultural sector. Therefore, the awareness of the frost time - occurrence has long been considered by researchers (Thom and Shaw, 1958; Rosenberg and Myers, 1962; Schmidlin, 1986; Watkins, 1991; Waylen, 1988). In order to manage the reduction of the effects of this destructive climate phenomenon on the agricultural sector and the exploitation of large regional environmental capabilities, it is necessary to notice seriously the detailed study of this phenomenon and its characteristics at the land level. And this will be costly and time-consuming. Therefore, with the purpose of preventing the last two factors and at the same time achieving managerial goals, it seems necessary to accurately zoning and recognizing homogeneity and non-homogeneity between different areas in a large area. Methodology In the first step, daily minimum temperature data were adjusted based on Julius day, and the averages of the five indicators including the day of the onset of frost, the day of the end of frost, the annual number of days of frost, the length of the frost season, and the length of the growing season were extracted. In the second step, the five indicators were modeled separately with three land-climate factors, namely altitude, longitude, and latitude of the stations, using multivariate regression models. To measure the accuracy of the obtained models, four basic assumptions were examined (). Using the regression models obtained for all parts of the province, the statistical indicators of the frosts were calculated and generalized to the points without stations. Finally, using the kiriging method, each of the five frost indicators of the province was zoned. Results and discussion The correlation coefficient of three variables, altitude, length, and latitude with different frost indices was obtained by simultaneously entering these three variables into the regression model. And four basic assumptions for measuring the accuracy of the obtained models were confirmed. The earliest occurrence of the first day of frost arises between September 21 and October 27, and in the mountains of northwestern Kurdistan, especially the Chehel Cheshmeh. The latest occurrence of the first day of frost also happens in the eastern lowlands of the province between October 17 and November 23. The earliest occurrence of the last day of frost arises between March 22 and 30 in the lowlands of southeastern and southwestern Kurdistan, and the latest happens between May 24 and June 1 in the high peaks of the west and northwest of the province, such as Chehel Cheshmeh Heights at an altitude of about 3173 meters, Ketresh Mountain with a height of 2592 meters, and Vazneh Mountain with a height of 2697 meters. The highest frequency of frost is in the mountains of the region with more than 196 days and the lowest frequency is in the eastern borders of the province with less than 72 days. The northwest mountains with 235 to 248 days and the eastern and southeastern regions of Kurdistan with 123 to 137 days, respectively, have the longest and shortest length of the frosted season. The longest growing season belongs to the eastern part of the province. The average growing season in this area is between 214 and 227 days. However, within this area, small sections that are lower in height have a longer growth period. On the other hand, the shortest growth period is in the western and northwestern mountains, averaging 116 to 129 days. Conclusion The results show that the three factors of altitude, latitude, and longitude can determine between 72 and 95% of the changes in different frost indicators. These three factors explain the 95, 90, 88, 80, and 72 percent changes in the length of the growth period, the occurrence of the first day of frost, the length of the frosted period, the frequency of frost, and the last day of frost, respectively. The Coefficient of determination is 95% for the first day of frost and 72% for the last day of frost. It seems that other factors besides the three mentioned factors play a role in changing the date of the last day of frost. Therefore, based on the studies of Noohi et al. in 2007, Noohi et al. 2009, and Alijani et al. in 2014, it can be inferred that the end frosts of the cold period can be more than the type of the advection frost. In other words, the synoptic factors can play a more important role in the occurrence of the last days of frost and its variability. But the spatial arrangement of different frost indices in Kurdistan province indicates a western to the eastern arrangement in the values of different frost indices. This means that with more movement from west to east, the number of frost days as well as the length of the frosted period decreases, and as a result, the growing season increases. In accordance with these changes, the occurrence of the first day and the last day of frost also arose with many delays between the eastern and western parts of the province. A comparison of the maps obtained from this algorithm showed that this method can provide more accurate details of the frost indicators compared to the zoning that used only stationary data (Mianabadi et al., 2009 and Ziaee et al. 2006).
Climatology
Hosein Rahmati; Samad Gholizadeh; Hosein Ansari
Abstract
Accurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara-Ariye ...
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Accurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara-Ariye basin with an area of 112 km2 and average annual rainfall of 306.72mm, two different models namely WetSpa and artificial neural network (ANN) were used. To run of the WetSpa model two categories of information, including raster maps and metrological data and for ANN model only meteorological data were used. The 5 years data were used to simulation runoff of Bara-Ariye basin. The statistical parameters such as correlation coefficient (R2), the square of the standard error of the mean (RMSE) and mean absolute error (MAE) were used for comparison results of two models. The results indicated that the WetSpa model with R2 and RMSE equal to 0.920 and 0.346 m3/s and also ANN model with R2 and RMSE equal to 0.959 and 0.310 m3/s have the ability to simulate runoff of Bara Ariye River. Also using neural network model reduced the error estimation of watershed runoff 11.6% compared with the WetSpa model.
Mohammadreza Pourmohammadi; Mohammadreza Karami
Volume 18, Issue 50 , February 2015, , Pages 55-88
Abstract
Evaluation of the vulnerability is one of the most important challenges facing metropolises in Iran. Sensitivity of the issue is duplicated when the city is not only timeworn and includes squatter areas, but also is exposed to natural disasters such as earthquake and flood. Zoning the risk of earthquake ...
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Evaluation of the vulnerability is one of the most important challenges facing metropolises in Iran. Sensitivity of the issue is duplicated when the city is not only timeworn and includes squatter areas, but also is exposed to natural disasters such as earthquake and flood. Zoning the risk of earthquake and its modeling by advanced techniques regarding the vulnerability level of cities is inevitable. In Tabriz, diversity of urban textures, proximity to fault lines and lack of precision and revision on the subject, increases the vulnerabilit of such textures besides squatter textures. This project studies the municipal areas (1 and 5) in Tabriz city, regarding the nature of earthquake and its relation with four factors: population density, building density, quality of buildings and types of materials. Furthermore, the relation of vulnerability due to earthquake has been studied and modeled taking the advantage of the GIS robust technique with integrating Kernel Density Estimation model (KDE) and Analytical Hierarchy Possess model (AHP) in order to determine the vulnerable areas more precisely with an emphasis on residential application.
