Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms
Article
Article Title | Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms |
---|---|
ERA Journal ID | 3551 |
Article Category | Article |
Authors | Barzegar, Rahim (Author), Moghaddam, Asghar Asghari (Author), Deo, Ravinesh (Author), Fijani, Elham (Author) and Tziritis, Evangelos (Author) |
Journal Title | Science of the Total Environment |
Journal Citation | 621, pp. 697-712 |
Number of Pages | 16 |
Year | 2018 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0048-9697 |
1879-1026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.scitotenv.2017.11.185 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0048969717332436 |
Abstract | Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g.conconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. |
Keywords | DRASTIC; groundwater contamination risk; Iran; machine learning model; multi-model |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
410402. Environmental assessment and monitoring | |
460207. Modelling and simulation | |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. This study was supported by USQ short-term ADOSP grant awarded to Dr R C Deo (Sept - Nov 2017). |
Byline Affiliations | University of Tabriz, Iran |
School of Agricultural, Computational and Environmental Sciences | |
University of Tehran, Iran | |
Soil and Water Resources Institute, Greece | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q47zw/mapping-groundwater-contamination-risk-of-multiple-aquifers-using-multi-model-ensemble-of-machine-learning-algorithms
890
total views11
total downloads0
views this month0
downloads this month