Zahra Barkhordari; jalal karami; Hojatolah Mahboobi
Abstract
Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is ofparticular importance. Improper pattern of water consumption in different areas of a city can be one ofthe cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order ...
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Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is ofparticular importance. Improper pattern of water consumption in different areas of a city can be one ofthe cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order toidentify consumption patterns in different areas of the city. The purpose of this study is to investigatethe spatial pattern of water consumption in Qom city using spatial autocorrelation techniques. For thisreason, the consumption of 117 neighborhoods of Qom city during 2017 was collected and theaverage household water consumption for each neighborhood was calculated. Moran index was usedto identify the type of consumption pattern and local Moran index and hot spot technique were usedfor spatial distribution of the consumption pattern. The results of spatial autocorrelation showed thatthe largest cluster pattern of water consumption in Qom city occurred in summer with the value ofMoran index (I = 0.24). Also, the highest significance of the index (z = 7.02) was observed in thisseason. In both local and hot spot analysis, it was observed that high consumption has a high clusterpattern compared to low consumption. Spatially, high consumption clusters were observed in thecentral and western neighborhoods of the city and low consumption clusters were observed in thesouthern, eastern and northern neighborhoods of the city. Temporally, high consumption clusters wereobserved in central and western neighborhoods in summer and winter, respectively and lowconsumption clusters were observed in cold seasons.
Sasan Alirezaei; Amir Sadeg Naghshineh; Jalal Karami
Abstract
Data collection and recording of Archaeological sites in Archaeological research is costly and requires a lot of manpower and time. Accordingly, the use of methods that can predict the presence of ancient monuments without direct observation will play a significant role in saving time and cost of Archaeological ...
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Data collection and recording of Archaeological sites in Archaeological research is costly and requires a lot of manpower and time. Accordingly, the use of methods that can predict the presence of ancient monuments without direct observation will play a significant role in saving time and cost of Archaeological surveys. The main issue of this research is to assess the ability of the logistic regression model to predict the dispersal of ancient sites in the Harsin-Bisotun plain. Predictor variables for this study include the environmental variables of slope, height, distance to river, vegetation, distance to modern cities, density of modern villages and distance to main roads, and dependent variable is the most turbulent of area in terms of existence prehistoric Archaeological sites. For modeling, using logistic regression, GIS and IDRISI softwares was used. By analyzing the results of the logistic regression model, results showed that, the logistic regression model was successful in prediction the dispersion of ancient sites in the Harsin-Bisotun plain. As well, the introduction of densly populated areas due to the presence of ancient sites as a modle-dependent variable in the plain-bound areas is more important than the mere introduction of GPS points of the ancient sites as a dependent variable. and, Accordingly, the cultural variability of village density in the Calcolithic Age, village density, distance to cites and the distance to main roads in the Bronze Age and distance to cites, distance to main roads in Iron Age have had the greatest impact in prediction the dispersion of ancient sites.
V Ahmadi; A Alimohammadi; J Karami
Volume 9, Issue 2 , December 2017, , Pages 61-78
Abstract
Management and planning of urban water supply in metropolis is very important. Development of the region urban and make cities to metropolis and increase of effective complex factor on water usage in the cities make consumption management and supply and distribution Water difficult. So rule extraction ...
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Management and planning of urban water supply in metropolis is very important. Development of the region urban and make cities to metropolis and increase of effective complex factor on water usage in the cities make consumption management and supply and distribution Water difficult. So rule extraction plays an important role in exploring patterns over data and decreasing complex. Rough Set Algorithm, which was developed in 1980s by Pawlak, is a powerful and flexible method to deal with uncertain and ambiguous data which has been used in this research to extract dominant rules over data set. The method used in this paper is combination of the rough set and genetic algorithms from data mining methods to develop rule extraction and data classification of water usage in Tehran city as the studying area. Socio-economic, environmental, time and water consumption and management zones have been used as the explanatory variables for prediction of the water use that database divided to 2 part 60% for result extraction and 40% as test set. Independent test sets have been used for evaluation of the accuracy of the extracted rules. Results have shown that, combination of the genetic algorithms and Rough Set leads to extraction of more reliable rules. Classification accuracy of the extracted rules from Rough sets was 77 percent. But optimization of rules by combination of the genetic algorithm with Rough sets, resulted in classification accuracy of 88 percent in 6th generation with average speed of convergence. By using the same speed of convergence in the accuracy increased to 92 percent in 10th generation. According to the extracted rules, important effective factors on annual water consumption are respectively the resident population, water price, population density, family size, spatial location (latitude), education levels, and per capita green spaces.
