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Predicting Concentrations of Selected Ions and Total Hardness in Groundwater Using Artificial Neural Networks and Multiple Linear Regression Models

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Date
2020
Author
Calvert, Matthew Brian
Advisor
Boadu, Fred K.
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Abstract

Assessing the quality of groundwater in a given aquifer can be an expensive and time-consuming process. An effort is made in this thesis to predict several water quality parameters, namely Fe, Cl, SO4, and total hardness (as CaCO3), from the easily measured properties total dissolved solids (TDS) and electrical conductivity (EC). This is achieved by establishing functional relationships between the four quality parameters, TDS, and EC using multiple linear regression (MLR) and artificial neural network (ANN) models. Data for these variables were gathered from five unrelated groundwater quality studies. Results indicate that the ANN models produced more accurate functions than MLR, showcasing the strength of ANN’s in predictive applications. Analysis of the relative importance of each parameter illustrates that total hardness (CaCO3) is most influential in determining TDS, while sulphate is most influential on EC. These results could have a significant impact on the future of groundwater quality assessments.

Description
Master's thesis
Type
Master's thesis
Department
Civil and Environmental Engineering
Subject
Water resources management
Artificial intelligence
Hydrologic sciences
Permalink
https://hdl.handle.net/10161/22223
Citation
Calvert, Matthew Brian (2020). Predicting Concentrations of Selected Ions and Total Hardness in Groundwater Using Artificial Neural Networks and Multiple Linear Regression Models. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/22223.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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