Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration
In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at opti...
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MDPI AG
2024-09-01
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| author | Divas Karimanzira |
| author_facet | Divas Karimanzira |
| author_sort | Divas Karimanzira |
| collection | DOAJ |
| description | In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a model region in Germany, our results demonstrate an 18% improvement in the prediction interval width. While traditional Bayesian CNN models may yield broader prediction intervals to adequately capture uncertainties, the 2DCNN-QD method prioritizes quality-driven interval optimization, resulting in narrower prediction intervals without sacrificing coverage probability. Notably, this approach is nonparametric, allowing it to be effectively utilized across a range of real-world scenarios. |
| format | Article |
| id | doaj-art-c35c2d5137e640dfb1c8a0bbaf1b5a02 |
| institution | Kabale University |
| issn | 2673-9585 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-c35c2d5137e640dfb1c8a0bbaf1b5a022024-12-27T14:34:42ZengMDPI AGKnowledge2673-95852024-09-014446248010.3390/knowledge4040025Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate ConcentrationDivas Karimanzira0Fraunhofer Institute for Optronics, System Technique and Image Exploitation IOSB, Am Vogelherd 90, 98693 Ilmenau, GermanyIn this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a model region in Germany, our results demonstrate an 18% improvement in the prediction interval width. While traditional Bayesian CNN models may yield broader prediction intervals to adequately capture uncertainties, the 2DCNN-QD method prioritizes quality-driven interval optimization, resulting in narrower prediction intervals without sacrificing coverage probability. Notably, this approach is nonparametric, allowing it to be effectively utilized across a range of real-world scenarios.https://www.mdpi.com/2673-9585/4/4/25uncertainty analysisspatial predictionregionalizationconvolutional neural networkshigh-quality definition |
| spellingShingle | Divas Karimanzira Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration Knowledge uncertainty analysis spatial prediction regionalization convolutional neural networks high-quality definition |
| title | Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration |
| title_full | Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration |
| title_fullStr | Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration |
| title_full_unstemmed | Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration |
| title_short | Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration |
| title_sort | probabilistic uncertainty consideration in regionalization and prediction of groundwater nitrate concentration |
| topic | uncertainty analysis spatial prediction regionalization convolutional neural networks high-quality definition |
| url | https://www.mdpi.com/2673-9585/4/4/25 |
| work_keys_str_mv | AT divaskarimanzira probabilisticuncertaintyconsiderationinregionalizationandpredictionofgroundwaternitrateconcentration |