A nonlinear modeling and forecasting system of earth fractures based on coupling of artificial neural network and geographical information system?exemplified by earth fractures in Yuci City, Shanxi, China

2003 ◽  
Vol 45 (1) ◽  
pp. 124-131 ◽  
Author(s):  
Qiang Wu ◽  
Siyuan Ye ◽  
Xiong Wu ◽  
Peipei Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographical Information System ◽  
Nonlinear Modeling ◽  
Geographical Information ◽  
Modeling And Forecasting ◽  
Forecasting System ◽  
Artificial Neural

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.

scholarly journals Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran

2018 ◽  
Vol 10 (10) ◽  
pp. 3376 ◽  
Author(s):  
Mohsen Alizadeh ◽  
Esmaeil Alizadeh ◽  
Sara Asadollahpour Kotenaee ◽  
Himan Shahabi ◽  
Amin Beiranvand Pour ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographic Information System ◽  
Social Vulnerability ◽  
Ann Model ◽  
Scatter Plots ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Tabriz City

This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters.

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