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) en Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification
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| 97 | "notes": "dentification of the origin of groundwater samples is not | 107 | "notes": "dentification of the origin of groundwater samples is not | ||
| 98 | always possible in complex multi\u0002layered aquifers. This poses a | 108 | always possible in complex multi\u0002layered aquifers. This poses a | ||
| 99 | major difficulty for a reliable interpretation of geochemical results. | 109 | major difficulty for a reliable interpretation of geochemical results. | ||
| 100 | The problem is especially severe when the information on the tubewells | 110 | The problem is especially severe when the information on the tubewells | ||
| 101 | design is hard to obtain. This paper shows a supervised classification | 111 | design is hard to obtain. This paper shows a supervised classification | ||
| 102 | method based on the Random Forest (RF) machine learning technique to | 112 | method based on the Random Forest (RF) machine learning technique to | ||
| 103 | identify the layer from where groundwater samples were extracted. The | 113 | identify the layer from where groundwater samples were extracted. The | ||
| 104 | classification rules were based on the major ion composition of the | 114 | classification rules were based on the major ion composition of the | ||
| 105 | samples. We applied this method to the Campo de Cartagena multi-layer | 115 | samples. We applied this method to the Campo de Cartagena multi-layer | ||
| 106 | aquifer system, in southeastern Spain. A large amount of | 116 | aquifer system, in southeastern Spain. A large amount of | ||
| 107 | hydrogeochemical data was avail\u0002able, but only a limited fraction | 117 | hydrogeochemical data was avail\u0002able, but only a limited fraction | ||
| 108 | of the sampled tubewells included a reliable determination of the | 118 | of the sampled tubewells included a reliable determination of the | ||
| 109 | bore\u0002hole design and, consequently, of the aquifer layer being | 119 | bore\u0002hole design and, consequently, of the aquifer layer being | ||
| 110 | exploited. Added difficulty was the very similar compositions of water | 120 | exploited. Added difficulty was the very similar compositions of water | ||
| 111 | samples extracted from different aquifer layers. Moreover, not all | 121 | samples extracted from different aquifer layers. Moreover, not all | ||
| 112 | groundwater samples included the same geochemical variables. Despite | 122 | groundwater samples included the same geochemical variables. Despite | ||
| 113 | of the difficulty of such a background, the Random Forest | 123 | of the difficulty of such a background, the Random Forest | ||
| 114 | classification reached accuracies over 90%. These results were much | 124 | classification reached accuracies over 90%. These results were much | ||
| 115 | better than the Lin\u0002ear Discriminant Analysis (LDA) and Decision | 125 | better than the Lin\u0002ear Discriminant Analysis (LDA) and Decision | ||
| 116 | Trees (CART) supervised classification methods. From a total of 1549 | 126 | Trees (CART) supervised classification methods. From a total of 1549 | ||
| 117 | samples, 805 proceeded from one unique identified aquifer, 409 | 127 | samples, 805 proceeded from one unique identified aquifer, 409 | ||
| 118 | proceeded from a possible blend of waters from several aquifers and | 128 | proceeded from a possible blend of waters from several aquifers and | ||
| 119 | 335 were of unknown origin. Only 468 of the 805 unique\u0002aquifer | 129 | 335 were of unknown origin. Only 468 of the 805 unique\u0002aquifer | ||
| 120 | samples included all the chemical variables needed to calibrate and | 130 | samples included all the chemical variables needed to calibrate and | ||
| 121 | validate the models. Finally, 107 of the groundwater samples of | 131 | validate the models. Finally, 107 of the groundwater samples of | ||
| 122 | unknown origin could be classified. Most unclassified samples did not | 132 | unknown origin could be classified. Most unclassified samples did not | ||
| 123 | feature a complete dataset. The uncertainty on the identification of | 133 | feature a complete dataset. The uncertainty on the identification of | ||
| 124 | training samples was taken in account to enhance the model. Most of | 134 | training samples was taken in account to enhance the model. Most of | ||
| 125 | the samples that could not be identified had an incomplete dataset.", | 135 | the samples that could not be identified had an incomplete dataset.", | ||
| 126 | "notes_translated": { | 136 | "notes_translated": { | ||
| 127 | "es": "dentification of the origin of groundwater samples is not | 137 | "es": "dentification of the origin of groundwater samples is not | ||
| 128 | always possible in complex multi\u0002layered aquifers. This poses a | 138 | always possible in complex multi\u0002layered aquifers. This poses a | ||
| 129 | major difficulty for a reliable interpretation of geochemical results. | 139 | major difficulty for a reliable interpretation of geochemical results. | ||
| 130 | The problem is especially severe when the information on the tubewells | 140 | The problem is especially severe when the information on the tubewells | ||
| 131 | design is hard to obtain. This paper shows a supervised classification | 141 | design is hard to obtain. This paper shows a supervised classification | ||
| 132 | method based on the Random Forest (RF) machine learning technique to | 142 | method based on the Random Forest (RF) machine learning technique to | ||
| 133 | identify the layer from where groundwater samples were extracted. The | 143 | identify the layer from where groundwater samples were extracted. The | ||
| 134 | classification rules were based on the major ion composition of the | 144 | classification rules were based on the major ion composition of the | ||
| 135 | samples. We applied this method to the Campo de Cartagena multi-layer | 145 | samples. We applied this method to the Campo de Cartagena multi-layer | ||
| 136 | aquifer system, in southeastern Spain. A large amount of | 146 | aquifer system, in southeastern Spain. A large amount of | ||
| 137 | hydrogeochemical data was avail\u0002able, but only a limited fraction | 147 | hydrogeochemical data was avail\u0002able, but only a limited fraction | ||
| 138 | of the sampled tubewells included a reliable determination of the | 148 | of the sampled tubewells included a reliable determination of the | ||
| 139 | bore\u0002hole design and, consequently, of the aquifer layer being | 149 | bore\u0002hole design and, consequently, of the aquifer layer being | ||
| 140 | exploited. Added difficulty was the very similar compositions of water | 150 | exploited. Added difficulty was the very similar compositions of water | ||
| 141 | samples extracted from different aquifer layers. Moreover, not all | 151 | samples extracted from different aquifer layers. Moreover, not all | ||
| 142 | groundwater samples included the same geochemical variables. Despite | 152 | groundwater samples included the same geochemical variables. Despite | ||
| 143 | of the difficulty of such a background, the Random Forest | 153 | of the difficulty of such a background, the Random Forest | ||
| 144 | classification reached accuracies over 90%. These results were much | 154 | classification reached accuracies over 90%. These results were much | ||
| 145 | better than the Lin\u0002ear Discriminant Analysis (LDA) and Decision | 155 | better than the Lin\u0002ear Discriminant Analysis (LDA) and Decision | ||
| 146 | Trees (CART) supervised classification methods. From a total of 1549 | 156 | Trees (CART) supervised classification methods. From a total of 1549 | ||
| 147 | samples, 805 proceeded from one unique identified aquifer, 409 | 157 | samples, 805 proceeded from one unique identified aquifer, 409 | ||
| 148 | proceeded from a possible blend of waters from several aquifers and | 158 | proceeded from a possible blend of waters from several aquifers and | ||
| 149 | 335 were of unknown origin. Only 468 of the 805 unique\u0002aquifer | 159 | 335 were of unknown origin. Only 468 of the 805 unique\u0002aquifer | ||
| 150 | samples included all the chemical variables needed to calibrate and | 160 | samples included all the chemical variables needed to calibrate and | ||
| 151 | validate the models. Finally, 107 of the groundwater samples of | 161 | validate the models. Finally, 107 of the groundwater samples of | ||
| 152 | unknown origin could be classified. Most unclassified samples did not | 162 | unknown origin could be classified. Most unclassified samples did not | ||
| 153 | feature a complete dataset. The uncertainty on the identification of | 163 | feature a complete dataset. The uncertainty on the identification of | ||
| 154 | training samples was taken in account to enhance the model. Most of | 164 | training samples was taken in account to enhance the model. Most of | ||
| 155 | the samples that could not be identified had an incomplete dataset." | 165 | the samples that could not be identified had an incomplete dataset." | ||
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