M. Gharekhani; A. A. Nadiri; A. Asghari Moghaddam
Abstract
Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary ...
Read More
Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary to manage the groundwater resources by identifying areas with high potential of contamination. In this study, groundwater vulnerability in Ardabil plain aquifer was evaluated by applying DRASTIC model. DRASTIC model was prepared by seven effective parameters on vulnerability, including groundwater depth, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity. These parameters were prepared as seven raster layers, and DRASTIC index was then calculated after ranking and weighting. The DRASTIC index value was obtained between 82 to 151 for the Ardabil plain. The main problem of this model is the subjectivity in determining rates and weights of the parameters. Therefore, the purpose of this study is to improve DRASTIC model using 5 methods of artificial intelligence (AI), such as Feedforward network (FFN), Recurrent neural network (RNN), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Committee machine (CM) to obtain the most accurate results of vulnerability evaluation. Because of heterogeneity in the Ardabil Plain, it is divided into 3 sections including west, east and north, and each section needs an individual model. For this purpose, the DRASTIC parameters and the vulnerability index were defined as inputs data and output data respectively for models, and nitrate concentration data were divided into two categories for training and test steps. The output of model in training step was corrected by the related nitrate concentration, and after model training, the output of model in test step was verified by the nitrate concentration. The results show that all of the artificial intelligence methods are able to improve the DRASTIC model, but the supervised committee machine artificial intelligence (SCMAI) model had the best results. According to this model, the most of high pollution potential areas located in western and northern parts of the plain, and need more protection.
M Nakhaei; M Vadiati; KH Mohammadi
Abstract
In the last few years, saline water intrusion in the Urmia aquifer has deteriorated groundwater quality. As a result of irremediable environmental impacts and deterioration of aquifer conditions, study on groundwater vulnerability due to saline water intrusion is very serious. This study focuses on the ...
Read More
In the last few years, saline water intrusion in the Urmia aquifer has deteriorated groundwater quality. As a result of irremediable environmental impacts and deterioration of aquifer conditions, study on groundwater vulnerability due to saline water intrusion is very serious. This study focuses on the application of the new method, GALDIT, for the assessment of aquifer vulnerability due to saline water intrusion in the Urmia Lake. The computing of the GALDIT index is based on six parameters:Groundwater occurrence (G), Aquifer hydraulic conductivity (A), High groundwater Level above sea level (L), Distance from shore (D), Impact of existing amplitude and extensive of saline water intrusion (I), and Thickness of aquifer (T). The results of this study showed GALDIT indices were very high, high, moderate, and low in the northeast, southeast, north, and low in east of the aquifer, respectively.The GALDIT approach assists to managers for evaluation of aquifer conditions.
K. Habibi; M. Behzadfar; A. Meshkini; S. Nazari
Abstract
Due to its geographic position and located on the World earthquake belt; Iran is always under threat from earthquakes and several shakes are recorded every year all over the country. The most recent earthquake with 6.8 degree magnitude on the Richter scale hit the city of Bam in 2003 and caused large ...
Read More
Due to its geographic position and located on the World earthquake belt; Iran is always under threat from earthquakes and several shakes are recorded every year all over the country. The most recent earthquake with 6.8 degree magnitude on the Richter scale hit the city of Bam in 2003 and caused large losses of human life and infrastructure. The 2003 Bam earthquake, with more than 30,000 casualties and 10,000 injuries, was the most Destructive earthquake in the current century in Iran. We aim to recognize the main reasons causing these deterioration problems. To this end, we first conceptualize thirteen physical-spatial factors. These factors are analyzed using fuzzy logic and IHPW (Inverse Hierarchy Process Weight) within Geographical Information System. We also attempt to identify the Correlation coefficient analyses between urban vulnerability and damage using Fuzzy logic and GIS. In statistics, correlation and dependence are any of a broad class of statistical relationships between two or more random variables or observed data values. With respect to the covariance between two variables (urban vulnerability map and damage post earthquake) the correlation coefficient is calculated 0.59. The results of the model as applied to the structures of the city of Bam illustrate that a fuzzy approach is a basic tool that can be used to identify urban vulnerability and damage post earthquake incident. Its application to the problem assists in unifying relevant theories and practices.
