Abstract
| Original language | English |
|---|---|
| Number of pages | 16 |
| Journal | Water Resources Management |
| Early online date | 11 Apr 2018 |
| DOIs | |
| Publication status | Early online date - 11 Apr 2018 |
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An Advanced Calibration Method for Image Analysis in Laboratory-Scale Seawater Intrusion Problems. / Robinson, G.; Moutari, S.; Ahmed, A. A.; Hamill, G. A.
In: Water Resources Management, 11.04.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - An Advanced Calibration Method for Image Analysis in Laboratory-Scale Seawater Intrusion Problems
AU - Robinson, G.
AU - Moutari, S.
AU - Ahmed, A. A.
AU - Hamill, G. A.
PY - 2018/4/11
Y1 - 2018/4/11
N2 - Image analysis is a useful tool for visualising flow through laboratory-scale aquifers but existing methods of converting image light intensity to concentration can be labour intensive and time consuming. The new approach proposed in this study utilises the Random Forest machine learning technique to build a calibration model to replace the requirement for unique calibrations of each test aquifer. Calibration images from a previous experimental study were used to train the Random Forest model and the output was compared to the results from ahigh resolution pixel-wise methodology. The Random Forest model provided a trade-off inaccuracy with increased efficiency and reduced sensitivity to image desynchronisation when compared to the pixel-wise method. The reduced accuracy was attributed in part to non-linear lighting distribution across the sandbox, which could be corrected by orientating the backlights effectively. Time savings of around 35% were achieved for this experimental study and this is expected to increase for larger scale studies. The new calibration approach exhibits some promising features in terms of its robustness to experiment
AB - Image analysis is a useful tool for visualising flow through laboratory-scale aquifers but existing methods of converting image light intensity to concentration can be labour intensive and time consuming. The new approach proposed in this study utilises the Random Forest machine learning technique to build a calibration model to replace the requirement for unique calibrations of each test aquifer. Calibration images from a previous experimental study were used to train the Random Forest model and the output was compared to the results from ahigh resolution pixel-wise methodology. The Random Forest model provided a trade-off inaccuracy with increased efficiency and reduced sensitivity to image desynchronisation when compared to the pixel-wise method. The reduced accuracy was attributed in part to non-linear lighting distribution across the sandbox, which could be corrected by orientating the backlights effectively. Time savings of around 35% were achieved for this experimental study and this is expected to increase for larger scale studies. The new calibration approach exhibits some promising features in terms of its robustness to experiment
U2 - 10.1007/s11269-018-1977-6
DO - 10.1007/s11269-018-1977-6
M3 - Article
JO - Water Resources Management
JF - Water Resources Management
SN - 0920-4741
ER -