Integration of artificial neural network and geographic information system applications in simulating groundwater quality

Background: Although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. Artificial neural network (ANN) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. Methods: In this study, ANN was applied to simulate groundwater quality and geographic information system (GIS) was used as pre-processing and post-processing tool in simulating water quality in the Mazandaran Plain (Caspian southern coasts, Iran). Groundwater quality was simulated using multilayer perceptron (MLP) network. The determination of groundwater quality index (GWQI) and the estimation of effective factors in groundwater quality were also undertaken. After modeling in ANN, the model validation was carried out. Also, the study area was divided with the pixels 1×1 km (raster format) in GIS medium. Then, the model input layers were combined and a raster layer which comprised the model inputs values and geographic coordinate was generated. Using geographic coordinate, the values of pixels (model inputs) were inputted into ANN (Neuro Solutions software). Groundwater quality was simulated using the validated optimum network in the sites without water quality experiments. In the next step, the results of ANN simulation were entered into GIS medium and groundwater quality map was generated based on the simulated results of ANN. Results: The results revealed that the integration of capabilities of ANN and GIS have high accuracy and efficiency in the simulation of groundwater quality. Conclusion: This method can be employed in an extensive area to simulate hydrologic parameters. Keywords: Water quality, GWQI, MLP, Mazandaran Plai

Development Models of Artificial Neural Network and Multiple Linear Regression for Predicting Compression Index and Compression Ratio for Soil Compressibility of Ramadi City

Artificial neural networks (ANN) as new techniques employed for the development of predictive models to estimate the needed parameters in geotechnical engineering to be used for comparison with laboratory and field tests and consequently reduce the cost, time, and effort. Flexible computing techniques are using an alternative statistical tool to analyze and evaluate experimental data from 102 consolidation tests on a variety of undisturbed soils from Ramadi city. The regression equations are developed to estimate the compression index and the compression ratio from index data. Multi-Layer Perceptron (MLP) network model is used to calculate compression index and a compression ratio of soils and comparing with the multiple linear regression statistical model MLR. It is found that the MLP showed a higher performance than MLR in predicting Cc and Cr and model accuracy between 0.81 to 16 percent. This will provide a good method for minimizing the potential inconsistency of correlations

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p