Application of multivariate approach in agrometeorological suitability zonation at northeast semiarid plains of Iran

Daneshvar, Mohammad Reza Mansouri; Bagherzadeh, Ali; Alijani, Bohloul

doi:10.1007/s00704-012-0827-3

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…The classification was achieved by hierarchical, agglomerative cluster analysis using Ward's method. Ward's method was selected, because it typically outperforms other algorithms in terms of separation, to give relatively dense clusters with small within group variance (Mansouri Daneshvar et al 2013). Based on the HCA, about nine distinct regions were recognized and were mapped by using GIS (Fig.…”

Section: Regionalization Of Mean Seasonal Precipitation Datamentioning

confidence: 99%

Regionalization of Precipitation Regimes in Iran Using Principal Component Analysis and Hierarchical Clustering Analysis

Darand

Daneshvar

2014

Environ. Process.

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Daily gridded precipitation data, between years 1951 and 2007, obtained from APHRODITE database, were analyzed to regionalize precipitation regimes in Iran country. The S-mode of principal component analysis (PCA) was applied on seasonal correlation matrix with eight derived variables. Based on eigenvalues over one, three factors were extracted between the components and varimax rotation was used to enhance interpretability of retained PCA scores. Then, hierarchical clustering analysis (HCA) was applied to group the homogeneous precipitation regimes. According to the HCA, nine distinct and homogenous regions were recognized. Then, the Kolmogorov-Smirnov test on seasonal percentage of precipitation distribution in these regions was used to identify the independent regimes which have been spatially mapped by using GIS. This study showed that the APHRODITE dataset potentially could be used for regionalization of precipitation regimes in Iran. According to the results, use of this dataset in order to group precipitation regimes is recommended for arid and semi-arid regions of mid-latitudes, especially in the Middle East countries.

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Section: Regionalization Of Mean Seasonal Precipitation Datamentioning

confidence: 99%

Regionalization of Precipitation Regimes in Iran Using Principal Component Analysis and Hierarchical Clustering Analysis

Darand

Daneshvar

2014

Environ. Process.

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“…Ultimately, a hierarchical clustering analysis (HCA) was used for validating the data correlations. HCA method, same as principal component analysis (PCA), is used to classify cases into subjective classes (clusters) based on similarities within a group and dissimilarities between groups of variables (Mansouri Daneshvar et al 2013). HCA method is known for its ability to divide the dataset into homogeneous and distinct groups (Shukla et al 2000).…”

Section: Discussionmentioning

confidence: 99%

A rapid method for evaluating the variables affecting traffic flow in a touristic road, Iran

Kardani-Yazd

Daneshvar³

2019

Environ Syst Res

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Background: This study aimed to evaluate variables that influence traffic flow and its contribution to touristic transportation in a touristic road, Iran. The traffic flow data were extracted from the hour-by-hour data in 2018 from a local traffic control center in addition to three daily indices, including climatic comfort indicator, temperature inversion indicator, and temporal indicator of local calendar events, which were obtained from databases and equations. The data time series were arranged into diurnal, weekly, and monthly scales to apply in correlation tests. Results: Results revealed that a rate of 18-25% of total transportation (about 3,500,000-5,000,000 vehicles from total 19,828,619 vehicles) was assumed as a touristic portion of the traffic flow in the study area. The relationships between independent variables and traffic flow data exposed the effective and considerable role of the local climate on the traffic flow at above 98% of confidence level, without a strong association between calendar effect and traffic flow. Statistical results in all temporal cycles revealed the significant positive and negative effects of the local climatic comfort index and temperature inversion index on the traffic flow, respectively. Conclusions: The finding of this study described that the traffic flow and touristic transportation in the study area are more affected by the local climatic comfort and temperature inversion indices, but are less affected by the local calendar holidays and occasional vacations. Hence, the decision-makers in the study area sternly need a fundamental climatic calendar for management, tourism transportation, and traffic flow instead of the current local calendar.

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“…Furthermore, the synchronized meteorological data were gathered from Table 1. After assessing the data quality, the transformed variables were coordinated, converged, and standardized before statistical analyses in order to overcome the effects caused by the scale differences of variables (Mansouri Daneshvar et al 2013). Then, to cluster the standardized variables, the proximity matrix was derived from distance correlation of SPSS based on dissimilarity measure of squared Euclidean distance and hierarchical cluster analysis (HCA).…”

Section: Methodology and Data Preparationmentioning

confidence: 99%

Climatic impacts on hydrogeochemical characteristics of mineralized springs: a case study of the Garab travertine zone in the northeast of Iran

Daneshvar

2014

Arab J Geosci

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In this study, we aimed to indicate that hydrogeochemical characteristics of the mineralized spring waters of the Garab travertine zone, in the northeast of Iran, are changed in accordance of regional climatic impacts. Some 18 hydrogeological and 8 meteorological variables from documented data of Garab water samples for the years 1970, 1984, 2001, and 2011 are employed. The temporal trends show that the years 1984 and 2011 are classified as the humid and wet periods and the years 1970 and 2001 are classified as the arid and dry periods in the study area. Our results revealed that the concentrations of Ca 2+ , Na + , K + , Cl − , and CO 3 2− were enhanced in the wet period by increasing carbonate precipitation and dissolution of surfacial Neogene Na-Cl soils. The recorded 8°C difference between water temperature and air temperature can be related to thermogene conditions of spring water and thick travertine deposits, while the same variability of water temperature and air temperature in the wet and dry periods exhibits meteogene conditions of spring water and tufa generation. The hydrogeochemical analysis revealed that the abundant values of electrical conductivity (~11,000 μs/ cm), total dissolved solids (~6,700 mg/l), and CO 2 (~350 mg/ l) together with the observed water type (Na-Cl and Ca-HCO 3 -SO 4 ) are due to thermal water origin and possible leakage of fossil seawaters in deeper ground.

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Application of multivariate approach in agrometeorological suitability zonation at northeast semiarid plains of Iran

Cited by 11 publications

References 15 publications

Regionalization of Precipitation Regimes in Iran Using Principal Component Analysis and Hierarchical Clustering Analysis

Regionalization of Precipitation Regimes in Iran Using Principal Component Analysis and Hierarchical Clustering Analysis

A rapid method for evaluating the variables affecting traffic flow in a touristic road, Iran

Climatic impacts on hydrogeochemical characteristics of mineralized springs: a case study of the Garab travertine zone in the northeast of Iran

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