Geographic Data
Bahram Imani; Jafar Jafarzadeh
Abstract
Extended Abstract Introduction Assessments of water quality have recently developed and now include surface and groundwater pollution issues. Permanent changes occurring in the quality of groundwater, especially those affecting drinking water and salinization of water sources, are considered to be a ...
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Extended Abstract Introduction Assessments of water quality have recently developed and now include surface and groundwater pollution issues. Permanent changes occurring in the quality of groundwater, especially those affecting drinking water and salinization of water sources, are considered to be a serious threat to rural development. Unfortunately, many people lack enough knowledge about the importance of groundwater and the harmful effects of environmental pollution on these valuable resources. The present study has investigated the quality of potable groundwater in the rural parts of central Ardabil County using multi-criteria decision-making models and geostatistical analysis in GIS environment. Parameters such as EC, PH, SO4-, Cl-, Na and TH (in terms of CaCo3) have been used to create an overall picture of the quality of potable groundwater resources in Ardabil County based on which related zoning map was developed in geographic information system. The kriging interpolation method was also used to obtain the spatial distribution of the parameters and the simple additive weighting method was used to weigh and rank the layers. According to the final map of water quality, approximately 36% of the central Ardabil County (around 88 square kilometers, mainly in the southern part of the study area) has access to optimal quality of drinking water. On the other hand, low quality of drinking water is observed in the northern and northeastern parts which cover 46% of the study area (112 square kilometers). Moreover, a direct relationship is observed between the population density and the density of deep and semi-deep wells and the decrease in the quality of water. Materials and MethodsThe present study has applied library research and field study methods. Rstudio and Arc GIS 10 software were also used to perform related analyses.Study AreaCase study area includes 243 square kilometers of the central Ardabil County consisting of three cities and nineteen villages as illustrated in Figure 1.The following methods were used in this research:1- Direct rating2- Kriging interpolation3- Standardization method4- Simple weighing method5- Fuzzification of the final dataThe following parameters have also been used to assess the quality of drinking water:1- Electrical conductivity (EC)2- Chlorine level (Cl-)3-The amount of sulfate (SO4+)4- The amount of nitrate (Na)5- Total water hardness (TH)6- Water acidity level (PH)Results & DiscussionGroundwater chemical quality is primarily assessed using parameters such as changes in the amount of dissolved salts, and limitations on various uses of water especially water used for drinking. Table 1 shows different types of conventional kriging methods selected through the method test for the parameters. These can be obtained using the mutual evaluation method and RMS error. Factors affecting the quality of drinking water are then ranked and weighted according to the expert opinions. The final quality map is thus prepared. Layers are then standardized in accordance with data description and the results are presented in Table 1. It also exhibits maximum permissible and desirable level of non-toxic chemicals in drinking water in accordance with the Iranian Standards and Industrial Research Institute (ISIRI) and the World Health Organization (WHO) standard, along with the maximum permissible level of mineral substances in drinking water. Semivariograms used for kriging interpolation are also presented. Table 2 shows the RMS and RMSE errors as well as the average standard error of the water quality parameters in the study area. The interpolated primary layers are presented in Figure 3. The final map prepared for the quality of potable water in the study area indicates that the quality of groundwater in the northern part and a little section of the central part (46% or 112 square kilometers of the study area) is unfavorable. This includes 8 villages of the County. 6 villages have access to drinking water with semi-optimal quality and 5 villages are located in the optimal area of water quality. Accordingly, the quality of potable groundwater decreases drastically as we move towards the northern and northeastern parts of the study area, and the lowest quality of groundwater is observed in the most northerly part of the study area (covering 46% of the study area). Figure 4 shows the density of deep and semi-deep wells, the amount of annual harvest from rivers in the central part of Ardabil (in thousand cubic meters per year), the population density and industrial areas in this region. A direct relationship is therefore observed between population density, the density of existing wells, the level of water extraction from rivers and the sharp drop in the quality of groundwater. According to the reports prepared by Ardabil Regional Water Company, around 32 million cubic meters of water is annually needed to meet the drinking requirements of urban and rural uses, which can seriously damage the quality of underground water in the area.ConclusionAccording to the final map of groundwater quality, only 36% of the study area (88 square kilometers) has access to drinking water with favorable quality which can be a great concern for planners and managers. Finally, it is suggested to use geostatistical methods and geographic information system as a useful tool to assess the quality of underground water. These methods can gradually replace the old methods and thus prepare more accurate statistics, increase the efficiency of water-related projects, and reduce their cost.
yousef ebadi; Javad Javdan; Mohammad Hossein Rezaei Moghaddam
Abstract
Extended Abstract Introduction Groundwater, its quantitative/qualitative variables, and variability directly affect human life, and thus has always been one of the major topics in scientific and academic research. Due to geographical, climatic and hydrological conditions, and specific patterns of surface ...
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Extended Abstract Introduction Groundwater, its quantitative/qualitative variables, and variability directly affect human life, and thus has always been one of the major topics in scientific and academic research. Due to geographical, climatic and hydrological conditions, and specific patterns of surface water and subsurface water resources exploitation, this country has always faced water scarcity. As a result of global and regional changes in temporal and spatial patterns of rainfall, this has intensified in recent years. Therefore, exploitation of groundwater resources has been considered as an option for supplying agricultural, industrial and drinking water. However, excessive exploitation of these resources will result in their destruction. In recent years, excessive removal of groundwater and reduction of groundwater levels have resulted in some problems like subsidence in some plains. This makes it necessary to study the quantitative and qualitative changes of these resources more clearly. Due to the complex nature of aquifers’ hydrogeological systems, accurate investigation of these resources seems costly and even impossible. Thus in order to achieve a better understanding, it is necessary to use different methods for estimation and evaluation of such variables. Material & Methods Most environmental features are completely continuous in nature, which makes it impossible to measure these features in every part of these environments. Thus, we can generalize measured samples to other areas lacking accurate measurements, and in this way estimate these variables in unmeasured areas. This is also true about quantitative and qualitative variables of groundwater, i.e. by collecting samples from some sections, we can measure different characteristics in these samples. This surface modelling -or in other words, generalization of points to surface- can be achieved with mathematical and statistical relationships and rules. Due to the spatial structure of the measured specimens, geo statistics is used in this regard. In recent years, artificial intelligence models, inspired by the natural nervous system and simulating its function, have yielded a very satisfactory result in groundwater estimation and studies. In order to evaluate the accuracy of geo statistical methods and artificial neural networks, the present study takes advantage of statistics and measurements collected from groundwater level of 46 wells in Shabestar-Sufiyan plain in 2014. Kriging method (geo statistics) and multilayer perceptron neural network method (MLP) were used along with error propagation pattern (BP) to estimate unmeasured features in the study area. MATLAB 2016B was used to perform the neural network modeling and ARCGIS10.5 was used to perform Kriging method and prepare the final maps. In both neural network and kriging models, geographical coordinates of observed wells was used as input and measured water table was introduced as the study goal. Primary data reduces the accuracy of models. Thus, data was normalized before being introduced to the neural network model. After the initial analysis of data dispersion and normalization, logarithmic transfer function was used due to the relative improvement of data in Kriging estimator model. Results & Discussion Results indicate that at the training and testing stage (with Sigmoid tangent activation function (Tansig) and 9 neurons in the middle layer), neural network method (MLP) with a high correlation coefficient (0.96) and root mean square error of 13.18 is more accurate than Kriging method with J-shaped Variogram model, a correlation coefficient of 0.90 and root mean square error of 20.10. Due to realistic results provided by neural network method, it is considered to be a more efficient method in estimation of water table in Shabestar-Sufiyan Plain. This is also consistent with earlier hydrogeological studies (regarding aquifers) performed on the ability and flexibility of Artificial Intelligence models. Conclusion Results obtained from the current research, and previous studies conducted in this field indicate that most artificial intelligence computing models are capable of evaluating and estimating continuous environmental variables. On the other hand, understanding groundwater resources’ conditions is considered to be crucial. Thus, new methods, such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference methods (ANFIS) and fuzzy inference systems (FIS), which provide greater accuracy can help decision makers and researchers in maintenance and improvement of the groundwater status.
Saeed Maleki; Ali Shojaeean; Ghasem Farahmand
Abstract
Extended Abstract
Introduction
Urban heating is one of the most well-known forms of local manipulation of the climate by mankind, so that changes in the use of land cover in urban areas can lead to an increase in urban temperatures relative to the air temperature in rural areas. This phenomenon has ...
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Extended Abstract
Introduction
Urban heating is one of the most well-known forms of local manipulation of the climate by mankind, so that changes in the use of land cover in urban areas can lead to an increase in urban temperatures relative to the air temperature in rural areas. This phenomenon has been quantified in the form of the Urban Heat Islands (UHI) and has been studied and recorded for over 150 years in various cities of the world. The effect of the Urban Heat Island refers to an increase in the temperature of each man-made area, with respect to the surrounding surfaces. This phenomenon in urban areas refers to an increase in the temperature of cities with respect to the rural and suburban areas. On the other hand, the heat island directly affects the health of urban wildlife. Each year, in the United States, about 1,000 animals die due to the temperature rise, and more than that are destroyed because of the urban air harmful compounds. These changes in the pattern of winds have very important and dangerous consequences, such as the transmission of air pollution and dispersed toxic particles from cities to the suburbs, to disruption the people’s comfort within the city, which is why the heat islands are now considered as the causes of worrying about people’s health. Moreover, the heat islands change the wind patterns in the cities and surrounding areas. The suburban breeze is a dominant phenomenon in cities that are located on a flat land. The presence of heat islands, in addition to temperature changes, causes changes in land processes such as early flourishing of urban plants and longer growing season.
Materials and Methods
The present research has been an applied research in terms of targeting and a field-analytical one in terms of data collection. In order to reach the final goal of the research, the meteorological statistics of the synoptic meteorological station of Urmia city was studied first. Then, the study of different regions of the city was done in terms of temperature given the 9 stations set up inside the city and the suburbs. The data of 9 stations set up in the city was adjusted by installing a dry temperature sensor at an altitude of 180 cm, in cooperation with the municipality of Urmia, at a minimum and maximum daily rate of two hours (7:30 am and 5:30 pm) in hourly, daily and monthly forms. It should be noted that, the desired statistical period is from April 21, 2015 to July 22, 2015, and the readout pattern is on a daily basis, and its output is in the form of 1st to 4th of each month (days 7, 15, 22 and 29 of each month).
Result and conclusion
The rapid growth of urbanization and the increase in the population of Urmia city has caused significant changes in the physical and natural conditions of the city. This increase and expansion of the urbanization trend has affected some of the meteorological quantities in a way that, the performed studies indicate that the minimum temperature of Urmia city during the twenty year period is increasing in all months of the year compared with the neighboring stations. Nevertheless, specifying the limits of the Urmia heat island requires more precise studies. The study of the isothermal map of the average maximum temperature in the months of May, June and July, 2015 indicates that the Velayat-e-Faqih square station with a temperature of 29.41 degrees Celsius accounts for the highest temperature compared with eight other stations and in fact, has formed the center of the heat island. At the same time, the station for the license plate exchange center in the city of Urmia with a maximum temperature of 22.27 Celsius, is the coolest station compared to other stations, indicating a heat difference of 6.64 Celsius in the city. According to the above map, the intensity of the heat island decreases by distancing from center of the city. But the most important result that can be obtained from the above maps is the extension of maximum temperature curve toward parts of the East and South-east. The reasons for the high average temperature at the station of the municipality town and the station of Golman Khane can be summarized as follows:
The existence of 90% of industrial uses, workshops and factories at the edge of these stations
Wind flow
Given that wind is the most effective barrier against the formation of heat islands, the combination of the wind field with the pattern of heat island’s spatial variations shows significant results, which is a sign of the great impact of wind on the quality of formation of the heat island. The wind contributes to the extension of the heat island’s curve through the transfer of suspended particles and gases existing in the urban atmosphere.
Seyyed Yaser Hakimdoust; Alimohammad Pourzeidi; Mohammad Saleh Gerami
Abstract
Introduction
Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal ...
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Introduction
Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal and spatial distribution of rainfall is a useful tool for understanding how non-uniform distribution of water resources and vegetation in each region takes place.Precipitation occurs when the wet weather and the climb factor exist both in the region.In other words, the wet air must rise to a certain height so that it can reach the saturation point due to the subsequent cooling down, and in the next, the cloud produces precipitation.The absence of any of these two factors prevents the occurrence of precipitation.
Rainfall variation is considered as a key factor in the structure and functioning of ecosystems, but its impact on scale and magnitude is much less than its spatial variation.The climatic element, especially precipitation, has significant changes in time periods.Therefore, the recognition of the element of precipitation as one of the two elements of the climate and its changes in different times and places allows the optimal utilization of the natural environment.The amountand spatial distribution of rainfall is a fundamental factor for decision making, design and evaluation of hydrological models as well as water management and planning.Temporal spatial variations have diverse and varied impacts on the management and planning of water resources along a water basin.Climate change is one of the factors affecting the change of water resources.Precipitation, as a highly variable element, has always been a concern for climatologists and waterologistsas a fundamental factor in the blue balance. The extreme variability of rainfall along the time-space has a variety of study approaches.The purpose of this research is to identify the conditions of rainfall in Mazandaran province. Therefore, the location of rainfallin this province was investigated.In this regard, identification of the effective factors of the occurrence of these rainfall in different seasons and their role in the province has been addressed and its results will be available as a scientific and practical solution.
Materials and Methods
In this study, for the purpose of identifying the rainfall in the province of Mazandaran, five years of rainfall from 2006 to 2010 have been used from a total of 12 synoptic stations.Using extracted data from precipitation graphs, rainfall of more than 10 mm was extracted in the studied area.Then the data were categorized into four parts: spring, summer, autumn, and winter of the year. To create the database, they entered the SPSS and ARC GIS10 software.In the spatial analysis of the data, the semi-modification of these models has been used, which was calculated using ARC GIS10 software.The methods used in the zoning of Kriging and IDW models for fitting include: IDW with three potentials of 1,2,3, and the Kriging method with spherical, circular, exponential, Gaussian, and spherical models, which is performed with conventional Kriging technique.Also, for statistical comparison of models, root mean square error of RMSE, MAE, RMSE and their correlation coefficient were used.Then, optimal mapping based on multivariate regression was fitted based on the simulation method and the recursive method of six variables in rainfall generation including latitude and longitude, number of rainfall days, elevation, relative humidity and dew point temperature. The effects of these factors on rainfall in the province will be evaluated in different seasons and annually.
ResultsandDiscussion
The results of the spring survey show that there were 5 stations out of 12 stations without rainfall.These stations are located in the plain and in the mountain range of the region.The analysis showed that the correlation coefficient between variables is R^2= 967, which indicates a strong relationship between the set of independent variables and the dependent variable.85.8% of rainfall in the spring season in Mazandaran province depend on these variables. In the summer, only 2 stations in the province did not experience rainfall ranges, both of which were at high altitudes and include the station Alasht and Kyasar.Variables show a very strong relationship in the summer with a correlation coefficientof R^2=0.995 which is 0.9. 9%of rainfall in Mazandaran province depends on these six variables.The fall season is one of the high seasons in the province of Mazandaran. Only one station (Siahbisheh) has been registered from 12 storm rainfall stations.Estimates show that the six variables analyzed in this chapter with a correlation coefficient of R^2 = 0.983 represent a strong correlation.The results of the winter season show that all stations in Mazandaran province have rainfall, although it includes fewer days than theautumn season.All stations experience at least one day at Alasht Station for up to 7 days in Ramsar.The results of the analysis show that in winter, the correlation coefficient is R^2 = 0.996.
Conclusion
For zoning of the study area, the IDW method with three potentials of 1, 2, 3 and the Kriging method have been used with spherical, circular, exponential and Gaussian models. The evaluation and determination of the best model and verification of the produced maps was carried out. Also, for statistical comparison of the models, the root mean square errors of RMS, MAE, RMSE and their correlation coefficient were used, which, the best model for zoning was the IDW model with two potentials of 1,3 and ordinary circular kriging. Optimal mapping was done by multivariate regression based on the model of synchronous and retrograde method, and six variables that have the greatest effect on rainfall, including latitude and longitude, rainfall days, elevation, relative humidity and dew point temperature were studied.The results show that the correlation values of these six variables are 0.97 in spring, 0.99 in summer, 0.98 in autumn, 0.99 in winter and 0.99 in annual rainfall which indicates a strong relationship between these six variables in the rainfall ofMazandaran province.
Fatemeh Mohammadyari; Hossein Aghdar; Reza Basiri
Abstract
Abstract[1]
Groundwater is of particular importance in arid and semi-arid areas.In this research, chemical properties of groundwater in arid and semi-arid regions of Mehran and Dehloran were studied using geo-statistical methods.Sodium, chlorine, sulfate, TDS and TH were evaluated variables.The ...
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Abstract[1]
Groundwater is of particular importance in arid and semi-arid areas.In this research, chemical properties of groundwater in arid and semi-arid regions of Mehran and Dehloran were studied using geo-statistical methods.Sodium, chlorine, sulfate, TDS and TH were evaluated variables.The semi-variogram of each parameters were calculated using GS + software and different models were fitted.After the normalization of the data, the variogram was plotted, and the interpolation was carried out by the method ofIDW and kriging in GIS software. The criterion for choosing an appropriate interpolation model was a lowerRMSE and a stronger spatial structure. The results show that the Kriging method is superior to the IDW method.Therefore maps were prepared using this method. The results show a strong correlation of the qualitative data of the region's water and the spatial structure is a Gaussian model function.Finally, by using fuzzy logic and Shouler classification, a zoning map of the area for drinking was prepared.According to the final map, 37% of the area is suitable for drinking, 13% is relatively suitable and 50% is inappropriate.As a result, the water quality of the area studied,is not desirable for drinking. Overlaying of the zoning map and the map resulted from the analysis of the obvious points showed that the points with high concentrations and on the threshold of the alert are placed side by side and in the wrong category of the zoning map.High levels of hardness rate and other elements in parts of the region are increasing.This is due to the substitution of alluvial deposits with Gachsaran Formation.Therefore, the main factor for the reductionof the water’s quality can be Gachsaran formation.
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