Mehrdad AhangarCani; Seyyed Hossien Khasteh
Abstract
Extended Abstract Introduction and Objective Due to the location of Iran in dry regions of the Middle East, and also because of the rapid increase in its urban population and water consumption, every day the issue of water scarcity becomes more severe in Iran. In recent years, Iran has faced serious ...
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Extended Abstract Introduction and Objective Due to the location of Iran in dry regions of the Middle East, and also because of the rapid increase in its urban population and water consumption, every day the issue of water scarcity becomes more severe in Iran. In recent years, Iran has faced serious water scarcity and excessive consumption of water resources. Therefore, patterns of urban water consumption, different geographic, spatial, demographic, social, and economic parameters, and the relation between these parameter and water consumption are considered to be among important issues affecting management of water resources. The present study seeks to investigate and analyze the spatial pattern of domestic water consumption in Babol County, and also to identify parameters affecting the pattern of water use. This is achieved by extracting association rules from some spatial and socio-economic parameters and based on the water use level in this County. The study also aims to determine regions with high/low level of water use, investigate spatial distribution of water consumption and finally, identify and categorize parameters affecting domestic water consumption at neighborhood level in this County using Decision Tree model. Materials and methods Data: Domestic water consumption data, census data, spatial and socio-economic parameters such as distance from main roads, distance from Babolrood, total area of garden and green space in each building, building site and standing property (total area of house yard), population density, total number of houses vs. apartments, number of housing units, average number of people per household, percentage of young/old people per household were extracted from the Statistical Center of Iran for the time period of 2011 to 2016. Then, these data were used to analyze urban water consumption in Babol County. Methods: Apriori algorithm - a data mining algorithm used to extract association rules- has been used to discover and extract relationships between different spatial socio-economic parameters and domestic water consumption patterns. Moreover, a decision tree has been developed which takes advantage of these parameters to predict domestic water use. Results and Discussion Results indicated that number of houses, number of household members, green space in each house, total area of house yard and distance from main roads are directly related with the household water consumption. On the other hand, population density, percentage of youth population, number of residential units and distance from Babolrood River are inversely related to domestic water consumption. Among all parameters considered in the present study, total area of house yard, distance from Babolrood River, number of residential units and number of household members exhibited a stronger relationship with water consumption. Thus, they were located on higher branches of the final decision tree. Additionally, results of global Moran’s I index indicated that there exists a spatial autocorrelation among household water consumption data. Moreover, this index indicated the clustered nature of residential water consumption distribution in Babol County. Also, spatial distribution of domestic water consumption in this County demonstrated that western and coastal areas with minimum distance from Babolrood River have the highest level of domestic water consumption. Therefore, it can be concluded that with an increase in distance from Babolrood River, domestic water consumption decreases. Only terraced and semi-detached houses exist in these neighborhoods. Thus compared to other neighborhoods, they have a lower population density, larger green space and larger yard. Conclusion and Future Works The present study applies Apriori algorithm to extract association rules and discover the relationship between spatial and socio-economic parameters and domestic water consumption. Results indicated that spatial and socio-economic parameters affect the spatial distribution of domestic water consumption in Babol County. Developing a decision tree, parameters associated with domestic water consumption were categorized and amount of water consumption was predicted. Extracted rules predicted domestic water consumption of test data with an accuracy of 75%. In this study, global Moran’s I index indicated the existence of a spatial autocorrelation among water consumption data. It also proves the clustered nature of domestic water consumption distribution in the study area. Additionally, spatial distribution of domestic water consumption in Babol County indicated that western and coastal neighborhoods have the highest level of domestic water consumption, while southern neighborhoods of Babol County have the lowest level of domestic water consumption. Model developed in the present study provides an opportunity for analyzing and predicting the level of water consumption. This will make planning for the reduction of water consumption and management of water resources possible. We suggest that future works evaluate the effect of other spatial and socio-economic parameters such as water cost and educational status of household members in a longer period (more than 5 years) to improve the accuracy of the model.
Mehrdad AhangarCani; Mahdi Farnaghi
Abstract
Introduction
Introduction and Objectives: Cutaneous Leishmaniasis (CL) is a vector-borne disease, endemic of the Middle East. The spread of CL is highly associated with the socio-ecological interactions of vectors, hosts and environmental conditions. CL is the most frequent vector-borne disease in Iran ...
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Introduction
Introduction and Objectives: Cutaneous Leishmaniasis (CL) is a vector-borne disease, endemic of the Middle East. The spread of CL is highly associated with the socio-ecological interactions of vectors, hosts and environmental conditions. CL is the most frequent vector-borne disease in Iran and especially in the north-eastern province, Golestan, which has long been known as one of the most important endemic areas for CL dispersion. Therefore, Golestan province was selected as the study area of this research. The main objectives of the study are to analyze annual spatial distribution of CL, investigate the relations between environmental/climate factors and incidence rate of CL and also provide a model to predict CL distribution at rural district level in Golestan province.
Materials and methods
Data: CL incidences, census data, environmental and climate factors have been used in this study to provide a model and produce a map to predict the CL distribution. The CL incidences are continuously recorded by the Center for Disease Control and Prevention (CDC) of Golestan province. The population and census data for 2013-2015 period were obtained from Iranian Statistical Center. Environmental and climate data such as vegetation, average humidity, average temperature, precipitation, number of rainy days, number of freezing days, maximum wind speed and evaporation rate were used as parameters affecting the model.
Methodology
The statistical and geo-statistical analyses were used to investigate the relation between environmental/climate factors and CL incidence rate, and to investigate the existence of spatial autocorrelation between CL cases, respectively. Additionally, Multilayer perceptron (MLP) neural network was used to model the relation between the distribution of CL incidences with environmental/climate factors, and also to generate the risk maps of CL. MLP is a type of neural network which consists of multiple layers of neurons or processing elements connected in a feed forward fashion. It encompasses three types of layers: input, hidden, and output. It has a unidirectional flow of information. Generally, information flow starts from input layer, goes through hidden layer, and then to output layer, which provides the response of the network to the input stimuli. In this type of network, there are generally three distinct types of neurons in layers. The input layer contains some neurons as the input variables. The hidden neurons, which are contained in one or more hidden layers, process and encode information within the network. The hidden layer receives, processes, and passes the input data to the output layer. Number of hidden layers and number of neurons within each layer affect the accuracy and functionality of the network. The output layer contains target output vector. In this study, effective parameters along with CL incidence rate of 2013-2014 were fed to the MLP as training data. The trained MLP was used afterward to generate the risk map of 2015 and test accuracy of the model. In order to determine the optimal parameters of the MLP, the grid-search and cross-validation techniques were used on 25% of the training dataset in the training phase. The performance of MLP was investigated using the root mean square error (RMSE), mean absolute percentage error (MAPE) and area under curve (AUC) of receiver operating characteristic (ROC) measures. Sensitivity analysis was also used to determine most effective variables regarding predictive mapping of CL distribution
Results and Discussion
Results of global Moran’s I index indicated that there is spatial autocorrelation among CL cases, and also distribution of CL cases in Golestan province in each 3 years is clustered. Moreover, statistical analyses showed that majority of the incidences belonged to rural districts of Gonbad-Kavos and Maraveh-Tappeh. Based on the results of statistical analyses (including Pearson correlation and Spearman rank correlation), positive correlations were observed between the CL incidence rate and average temperature, maximum wind speed and evaporation. In addition, negative correlation was found between the CL incidence rate and average humidity, precipitation, number of rainy days, number of freezing days and vegetation. According to the results of evaluation criteria including RMSE, MAPE and AUC, the trained MLP model was able to generate risk maps of CL in 2013-2015 for each rural district with acceptable accuracy. Additionally, results of sensitivity analysis indicate that vegetation and average humidity are the most influencing variables in the incidence of CL and in predictive mapping of CL distribution in Golestan province.
Conclusion and Future works
In this study, the global Moran’s I index indicated the presence of spatial autocorrelation among CL cases, and clustered distribution of disease in the study area. The statistical analyses showed that environmental and climate factors greatly affect the spatial distribution of CL. The MLP method, used to generate CL distribution risk maps, was able to generate the study area risk maps with acceptable accuracy. Results highlight the potential high risk areas requiring special plans and resources for monitoring and control of the disease. As a future work, we suggest that the effects of other environmental and socio-economic parameters should be evaluated to improve the accuracy of the model. It is also recommended that other methods such as regression and other neural network techniques be used to generate CL risk maps.
Hassan Khosravi; Esmail Haydari Alamdarloo; sahar nasabpour
Abstract
Extended Abstract
Introduction
Water is the principal source of economic development, social security and poverty reduction. The value of water source leads to enhancement of management measures to maintain its quality and quantity by communities. Environmental changes and human activities effect on ...
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Extended Abstract
Introduction
Water is the principal source of economic development, social security and poverty reduction. The value of water source leads to enhancement of management measures to maintain its quality and quantity by communities. Environmental changes and human activities effect on the quality and quantity of water. Urban growth, increasing industrial activities and overuse of chemical fertilizers in agriculture result in pollution of groundwater and surface water which have adverse effect on the health of human beings, animal and plants. Groundwater is the primary source to supply agriculture and drinking water hence recognition and awareness of groundwater quality and the water classification based on the number of various elements of them will assist us in making management decisions and decline groundwater pollution. Groundwater is particularly important in arid and semi-arid areas. On the other hand groundwater deterioration, both in quantitative and qualitative terms is important in water resources management of these areas.
The concentration of common ions in water is one index for assessing water quality. Groundwater quality index (GQI), a combination of parameters for water quality, that constitute a reliable tool in defining aquifer vulnerability is used to investigate the spatial variability. GQI shows the data related to the water quality in an explicit manner. This index presents a way of summarizing the overall qualitative condition of water which is understandable for the audience.
Materials&Methods
Yazd-Ardakan plain has been faced with significant reduction of groundwater level in recent years. So, it is expected that the studying groundwater quality index can be effective for aquifer management in this plain. In this research GQI was used in order to study the distribution of major water ions including Calcium, Magnesium, Sodium, Chloride, Sulfate and Total Dissolved Solids (TDS); and zoning groundwater quality using ArcGIS10.1. The data of 53 piezometric wells provided by Iran Water Resources Management Company were used to study the status of temporal and spatial changes of GQI in Yazd-Ardakan plain. Water quality sampling campaigns were conducted during most vulnerable periods of early and late summer to ensure the representativeness of the targeted GQI under worst case conditions. Quality zoning maps were provided for 2003, 2006 and 2011. For this purpose, data were evaluated in GS+ 5.1 software, after calculation, the best model with the lowest estimated error was selected for zoning water quality parameters. Because of the lowest estimation error, Kriging, Gaussian and Spherical variogram models were selected as appropriate interpolation method for zoning the quality parameters. WHO standards were used to compare and investigate the quality status of the water. The water qualitative groups in the GQI map were divided into 5 classes of good, acceptable, medium, inappropriate, and poor, scoring from 0 to 100. The class which is close to 100 shows better quality and the class which is close to 0 shows lower quality.
Results & Discussion
The results showed that Yazd-Ardakan plain is located in average and acceptable classes according to GQI index. The highest and lowest amount of Groundwater Quality Index (GQI) were in the west and north of the study area, respectively. Moran's I spatial autocorrelation index, GQI and all chemical studied variables except for magnesium have cluster spatial distribution pattern, but Magnesium have random spatial distribution pattern. Three parameters of Total Dissolved Solids (TDS), Sodium and Chloride having the highest coefficient ranking have a highest impact on GQI, respectively. On the other hand, these parameters have a high weight and GQI is more sensitive to them. In fact, these components in Yazd-Ardakan plain groundwater have more impacts on GQI model and their removal will cause greater changes in GQI. Therefore, they should be carefully evaluated and monitored. Generally, it can be concluded that GQI has the descending trend in the study area and land use has the important role in reducing GQI index and water quality. It can also explain the overall quality of groundwater and its threats in various uses of water. Finally, the regions with poor groundwater quality can be targeted for detailed studies and monitoring programs.
Conclusion
According to the results, due to the nature of natural phenomena such as drought, their complete removal is not possible. The only principled way to prevent dangerous consequences of the water table decline and reduction of groundwater resources quantity and quality is the correct and systematic use of water and avoidance of uncontrolled groundwater withdrawal.
Seyyed Eskandar Seydaei Seyyed Eskandar Seydaei; Seyyedeh Somayeh Hosseini
Abstract
Abstract
In the sustainable tourism development approach, not only market needs are considered, but also the needs of society and the natural environment are emphasized. In this regard, GIS can be used for many tourist, planning and modeling activities. The present study aimed to provide a model for ...
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Abstract
In the sustainable tourism development approach, not only market needs are considered, but also the needs of society and the natural environment are emphasized. In this regard, GIS can be used for many tourist, planning and modeling activities. The present study aimed to provide a model for access to tourist attractions for the sustainability in tourist hubs of Isfahan province. Regarding the studied components, this research is an applied one in terms of type and a descriptive-survey in terms of method. This research was conducted based on the closest access considering the speed of access (road quality) in terms of minutes, and in order to provide a model for the stability of areas with the highest attraction and concentration of tourist in a zone rather than a point. The cities of Isfahan and Kashan (historical-cultural) and the cities of Semirom and Fereydoon Shahr (natural tourism) have the highest tourist capacity in the province, respectively. The results of the study show that according to the "Closest Access" model, five classes have been taken into consideration for the sustainability of the four tourist hubs in the province. The first class covers the potential rural and urban locations to a 60 minute radius (according to experts, the 60 minute distance is the distance that the tourists tend to travel by car on the way to the desired attraction) from the natural tourist hubs, the West (Fereydoon Shahr), the Southwest (Semirom) and the historical-cultural tourist hubs of Isfahan and Kashan which should be addressed by the authorities and tourism planners of the province to instruct tourists.
