Web-based Data Analytics Framework for Well Forecasting and Groundwater Quality

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Authors

Muhammed Ali Sit , Richard J. Langel, Darrin Thompson, David M. Cwiertny, Ibrahim Demir

Abstract

Groundwater supplies drinking water for over one-third of all Americans. However, with aquifers stressed by overdraft, contamination from land use, and the hydrologic impacts of climate change, identifying reliable sources for new wells is increasingly challenging. Well forecasting is a process in which potential groundwater resources are evaluated for a location of interest. While this process forecasts the depth of each aquifer for a given location, it takes historical groundwater well data from nearby locations into account. Conventionally, well forecasting is done by geological survey professionals by manually analyzing the well data and, that makes the process both time and resource-intensive. This study presents a novel web application that performs well forecasting for any location within the state of Iowa in a matter of seconds utilizing client-side computing instead of expensive professional labor. The web application generates well forecasts by triangulating millions of combinations of historical aquifer depth data of nearby wells stored in a state-level database. The proposed web system also provides water quality information for arsenic, nitrate, and bacteria (total c and fecal coliform) on the same interface with forecasts. The system is open to the public and is aimed to provide a go-to tool for homeowners, well drillers and, authorities to help inform decision-making regarding groundwater well development and water quality monitoring efforts.

DOI

https://doi.org/10.31223/osf.io/e5avq

Subjects

Civil and Environmental Engineering, Engineering, Environmental Engineering, Hydraulic Engineering

Keywords

aquifer depth prediction, groundwater wells, web application

Dates

Published: 2020-08-16 19:35

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License

GNU Lesser General Public License (LGPL) 2.1

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