Salmonellainfections modelling in Mississippi using neural network and geographical information system (GIS)

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.

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A neural network correlator for satellite imagery and ground truth data in a geographical information system

[Proceedings] 1991 IEEE International Joint Conference on Neural Networks ◽

10.1109/ijcnn.1991.170464 ◽

1991 ◽

Author(s):

X. Wu ◽

J. Westervelt

Keyword(s):

Neural Network ◽

Information System ◽

Geographical Information System ◽

Satellite Imagery ◽

Ground Truth ◽

Geographical Information ◽

Ground Truth Data

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

Association of Arab Universities Journal of Engineering Sciences ◽

10.33261/jaaru.2019.26.1.021 ◽

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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