scholarly journals Spatio-Temporal Modeling of Ozone Distribution in Tehran, Iran Based on Neural Network and Geographical Information System

2022 ◽  
Author(s):  
Leila Sherafati ◽  
Hossein Aghamohammadi Zanjirabad ◽  
Saeed Behzadi
Keyword(s):  
Neural Network ◽  
Information System ◽  
Geographical Information System ◽  
Meteorological Parameters ◽  
Interpolation Method ◽  
Temporal Analysis ◽  
Geographical Information ◽  
Temporal Modeling ◽  
Spatiotemporal Model ◽  
Ozone Distribution

Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS). Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017. Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O3 concentration.

scholarly journals Mapping Climate Changes in Iraq By Using Geographical Information System (GIS)

2018 ◽  
Vol 7 (4.20) ◽  
pp. 578 ◽  
Author(s):  
Zainab Sahib Jawad ◽  
Fatima Asaad Tayeb ◽  
Asaad Tayeb Kareem Jebur
Keyword(s):  
Climate Change ◽  
Information System ◽  
Greenhouse Gases ◽  
Geographical Information System ◽  
Air Temperature ◽  
Greenhouse Effect ◽  
Interpolation Method ◽  
Geographical Information ◽  
Spatial Interpolation Method ◽  
Carbon Dioxide Co2

The Trapped sun’s thermal radiation in the earth’s atmosphere is known as the greenhouse effect.  This process is considered very important since it keeps the earth warm and hence possible to live in. Greenhouse gases such as carbon dioxide (CO2) and methane (CH4) are considered very important contributors to the greenhouse effect. During the last two decades, the level of greenhouse gases has increased, which plays a major role in global warming and climate change. The Middle East is considered among the most affected areas by climate change. In the current study, Geographical Information System (GIS) has been used to create some temperature maps that could show the air temperature distribution and difference between two different periods of time (past and recent) in different stations that cover the Iraqi governorates. A spatial interpolation method has been used. This method considers known values of temperature at a given location (stations in the current study) to estimate a continuous surface map during a specific period of time. The results of this study showed no significant increase in the average air temperature values, however the area of high air temperature values is growing during the cold and hot months of the year.  

scholarly journals Application of Artificial Neural Network and Geographical Information System Models to Predict and Evaluate the Quality of Diyala River Water, Iraq

2019 ◽  
Vol 26 (1) ◽  
pp. 160-169
Author(s):  
Nawar Omran Al-Musawi ◽  
Fatima Muqdad Al-Rubaie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographical Information System ◽  
Geographical Information ◽  
Total Alkalinity ◽  
Artificial Neural ◽  
Diyala River

This research discusses application Artificial Neural Network (ANN) and Geographical Information System (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameters were used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium, Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity. These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period 2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala province while it is poor to very polluted at the south of Baghdad City. The selected parameters were subjected to Kruskal-Wallis test for detecting factors contributing to the degradation of water quality and for eliminating independent variables that exhibit the highest contribution in p-value. The analysis of results revealed that ANN model was good in predicting the WQI. The confusion matrix for Artificial Neural Model (NNM) gave almost 96% for training, 85.7% for testing and 100% for holdout. In relation to GIS, six color maps of the river have been constructed to give clear images of the water quality along the river.

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