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En el instante 23 de junio de 2026, 16:14:09 UTC,
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Modificado el valor del campo
spatial_coverage
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en Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain)
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| 91 | "notes": "The goal of this research is to develop accurate and | 91 | "notes": "The goal of this research is to develop accurate and | ||
| 92 | reliable forecasting models for chlorophyll \u0251 concentrations in | 92 | reliable forecasting models for chlorophyll \u0251 concentrations in | ||
| 93 | seawater at multiple depth levels in El Mar Menor (Spain). Chlorophyll | 93 | seawater at multiple depth levels in El Mar Menor (Spain). Chlorophyll | ||
| 94 | \u0251 can be used as a eutrophication indicator, which is especially | 94 | \u0251 can be used as a eutrophication indicator, which is especially | ||
| 95 | essential in a rich yet vulnerable ecosystem like the study area. | 95 | essential in a rich yet vulnerable ecosystem like the study area. | ||
| 96 | Bayesian regularized artificial neural networks and Long Short-term | 96 | Bayesian regularized artificial neural networks and Long Short-term | ||
| 97 | Memory Neural Networks (LSTMs) employing a rolling window approach | 97 | Memory Neural Networks (LSTMs) employing a rolling window approach | ||
| 98 | were used as forecasting algorithms with a one-week prediction | 98 | were used as forecasting algorithms with a one-week prediction | ||
| 99 | horizon. Two input strategies were tested: using data from the own | 99 | horizon. Two input strategies were tested: using data from the own | ||
| 100 | time series or including exogenous variables among the inputs. In this | 100 | time series or including exogenous variables among the inputs. In this | ||
| 101 | second case, mutual information and the | 101 | second case, mutual information and the | ||
| 102 | Minimum-Redundancy-Maximum-Relevance approach were utilized to select | 102 | Minimum-Redundancy-Maximum-Relevance approach were utilized to select | ||
| 103 | the most relevant variables. The models obtained reasonable results | 103 | the most relevant variables. The models obtained reasonable results | ||
| 104 | for the univariate input scheme with \u03c3\u00af\u00af\u00af\n | 104 | for the univariate input scheme with \u03c3\u00af\u00af\u00af\n | ||
| 105 | values over 0.75 in levels between 0.5 and 2 m. The inclusion of | 105 | values over 0.75 in levels between 0.5 and 2 m. The inclusion of | ||
| 106 | exogenous variables increased these values to above 0.85 for the same | 106 | exogenous variables increased these values to above 0.85 for the same | ||
| 107 | depth levels. The models and methodologies presented in this paper can | 107 | depth levels. The models and methodologies presented in this paper can | ||
| 108 | constitute a very useful tool to help predict eutrophication episodes | 108 | constitute a very useful tool to help predict eutrophication episodes | ||
| 109 | and act as decision-making tools that allow the governmental and | 109 | and act as decision-making tools that allow the governmental and | ||
| 110 | environmental agencies to prevent the degradation of El Mar Menor.", | 110 | environmental agencies to prevent the degradation of El Mar Menor.", | ||
| 111 | "notes_translated": { | 111 | "notes_translated": { | ||
| 112 | "es": "The goal of this research is to develop accurate and | 112 | "es": "The goal of this research is to develop accurate and | ||
| 113 | reliable forecasting models for chlorophyll \u0251 concentrations in | 113 | reliable forecasting models for chlorophyll \u0251 concentrations in | ||
| 114 | seawater at multiple depth levels in El Mar Menor (Spain). Chlorophyll | 114 | seawater at multiple depth levels in El Mar Menor (Spain). Chlorophyll | ||
| 115 | \u0251 can be used as a eutrophication indicator, which is especially | 115 | \u0251 can be used as a eutrophication indicator, which is especially | ||
| 116 | essential in a rich yet vulnerable ecosystem like the study area. | 116 | essential in a rich yet vulnerable ecosystem like the study area. | ||
| 117 | Bayesian regularized artificial neural networks and Long Short-term | 117 | Bayesian regularized artificial neural networks and Long Short-term | ||
| 118 | Memory Neural Networks (LSTMs) employing a rolling window approach | 118 | Memory Neural Networks (LSTMs) employing a rolling window approach | ||
| 119 | were used as forecasting algorithms with a one-week prediction | 119 | were used as forecasting algorithms with a one-week prediction | ||
| 120 | horizon. Two input strategies were tested: using data from the own | 120 | horizon. Two input strategies were tested: using data from the own | ||
| 121 | time series or including exogenous variables among the inputs. In this | 121 | time series or including exogenous variables among the inputs. In this | ||
| 122 | second case, mutual information and the | 122 | second case, mutual information and the | ||
| 123 | Minimum-Redundancy-Maximum-Relevance approach were utilized to select | 123 | Minimum-Redundancy-Maximum-Relevance approach were utilized to select | ||
| 124 | the most relevant variables. The models obtained reasonable results | 124 | the most relevant variables. The models obtained reasonable results | ||
| 125 | for the univariate input scheme with \u03c3\u00af\u00af\u00af\n | 125 | for the univariate input scheme with \u03c3\u00af\u00af\u00af\n | ||
| 126 | values over 0.75 in levels between 0.5 and 2 m. The inclusion of | 126 | values over 0.75 in levels between 0.5 and 2 m. The inclusion of | ||
| 127 | exogenous variables increased these values to above 0.85 for the same | 127 | exogenous variables increased these values to above 0.85 for the same | ||
| 128 | depth levels. The models and methodologies presented in this paper can | 128 | depth levels. The models and methodologies presented in this paper can | ||
| 129 | constitute a very useful tool to help predict eutrophication episodes | 129 | constitute a very useful tool to help predict eutrophication episodes | ||
| 130 | and act as decision-making tools that allow the governmental and | 130 | and act as decision-making tools that allow the governmental and | ||
| 131 | environmental agencies to prevent the degradation of El Mar Menor." | 131 | environmental agencies to prevent the degradation of El Mar Menor." | ||
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| 265 | "uri": | 236 | "uri": | ||
| 266 | atos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia" | 237 | atos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia" | ||
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| 269 | "spatial_uri": | 240 | "spatial_uri": | ||
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| 271 | "state": "active", | 242 | "state": "active", | ||
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| 273 | "es": "" | 244 | "es": "" | ||
| 274 | }, | 245 | }, | ||
| 275 | "tag_uri": [ | 246 | "tag_uri": [ | ||
| 276 | 247 | ||||
| 277 | tp://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment" | 248 | tp://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment" | ||
| 278 | ], | 249 | ], | ||
| 279 | "tags": [ | 250 | "tags": [ | ||
| 280 | { | 251 | { | ||
| 281 | "display_name": "aguas_interiores", | 252 | "display_name": "aguas_interiores", | ||
| 282 | "id": "ddab08b5-a7e4-4187-9b0f-d4459c83a9ba", | 253 | "id": "ddab08b5-a7e4-4187-9b0f-d4459c83a9ba", | ||
| 283 | "name": "aguas_interiores", | 254 | "name": "aguas_interiores", | ||
| 284 | "state": "active", | 255 | "state": "active", | ||
| 285 | "vocabulary_id": null | 256 | "vocabulary_id": null | ||
| 286 | }, | 257 | }, | ||
| 287 | { | 258 | { | ||
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| 289 | "id": "7af4b5b1-a152-48cb-b581-a321fbe3ff88", | 260 | "id": "7af4b5b1-a152-48cb-b581-a321fbe3ff88", | ||
| 290 | "name": "analisis_espacial", | 261 | "name": "analisis_espacial", | ||
| 291 | "state": "active", | 262 | "state": "active", | ||
| 292 | "vocabulary_id": null | 263 | "vocabulary_id": null | ||
| 293 | }, | 264 | }, | ||
| 294 | { | 265 | { | ||
| 295 | "display_name": "biomarcadores", | 266 | "display_name": "biomarcadores", | ||
| 296 | "id": "abb5fd87-d584-42a3-bbd7-833940168687", | 267 | "id": "abb5fd87-d584-42a3-bbd7-833940168687", | ||
| 297 | "name": "biomarcadores", | 268 | "name": "biomarcadores", | ||
| 298 | "state": "active", | 269 | "state": "active", | ||
| 299 | "vocabulary_id": null | 270 | "vocabulary_id": null | ||
| 300 | }, | 271 | }, | ||
| 301 | { | 272 | { | ||
| 302 | "display_name": "eutrofizacion", | 273 | "display_name": "eutrofizacion", | ||
| 303 | "id": "5fcdd244-0f78-4801-be7d-5a67c5bb55ec", | 274 | "id": "5fcdd244-0f78-4801-be7d-5a67c5bb55ec", | ||
| 304 | "name": "eutrofizacion", | 275 | "name": "eutrofizacion", | ||
| 305 | "state": "active", | 276 | "state": "active", | ||
| 306 | "vocabulary_id": null | 277 | "vocabulary_id": null | ||
| 307 | }, | 278 | }, | ||
| 308 | { | 279 | { | ||
| 309 | "display_name": "fotosintesis", | 280 | "display_name": "fotosintesis", | ||
| 310 | "id": "4a120a32-4ed4-4b61-9193-1d46da5f3b4e", | 281 | "id": "4a120a32-4ed4-4b61-9193-1d46da5f3b4e", | ||
| 311 | "name": "fotosintesis", | 282 | "name": "fotosintesis", | ||
| 312 | "state": "active", | 283 | "state": "active", | ||
| 313 | "vocabulary_id": null | 284 | "vocabulary_id": null | ||
| 314 | }, | 285 | }, | ||
| 315 | { | 286 | { | ||
| 316 | "display_name": "marino", | 287 | "display_name": "marino", | ||
| 317 | "id": "004084d1-68d8-46ca-842a-c10278540bc9", | 288 | "id": "004084d1-68d8-46ca-842a-c10278540bc9", | ||
| 318 | "name": "marino", | 289 | "name": "marino", | ||
| 319 | "state": "active", | 290 | "state": "active", | ||
| 320 | "vocabulary_id": null | 291 | "vocabulary_id": null | ||
| 321 | }, | 292 | }, | ||
| 322 | { | 293 | { | ||
| 323 | "display_name": "tecnologia", | 294 | "display_name": "tecnologia", | ||
| 324 | "id": "0b3381bc-073c-43fe-a133-4efcf0e36300", | 295 | "id": "0b3381bc-073c-43fe-a133-4efcf0e36300", | ||
| 325 | "name": "tecnologia", | 296 | "name": "tecnologia", | ||
| 326 | "state": "active", | 297 | "state": "active", | ||
| 327 | "vocabulary_id": null | 298 | "vocabulary_id": null | ||
| 328 | } | 299 | } | ||
| 329 | ], | 300 | ], | ||
| 330 | "thematic_area": [ | 301 | "thematic_area": [ | ||
| 331 | "espacios_protegidos" | 302 | "espacios_protegidos" | ||
| 332 | ], | 303 | ], | ||
| 333 | "theme_es": [ | 304 | "theme_es": [ | ||
| 334 | "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente" | 305 | "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente" | ||
| 335 | ], | 306 | ], | ||
| 336 | "title": "Deep Learning Approach for the Prediction of the | 307 | "title": "Deep Learning Approach for the Prediction of the | ||
| 337 | Concentration of Chlorophyll \u0251 in Seawater. A Case Study in El | 308 | Concentration of Chlorophyll \u0251 in Seawater. A Case Study in El | ||
| 338 | Mar Menor (Spain)", | 309 | Mar Menor (Spain)", | ||
| 339 | "title_translated": { | 310 | "title_translated": { | ||
| 340 | "es": "Deep Learning Approach for the Prediction of the | 311 | "es": "Deep Learning Approach for the Prediction of the | ||
| 341 | Concentration of Chlorophyll \u0251 in Seawater. A Case Study in El | 312 | Concentration of Chlorophyll \u0251 in Seawater. A Case Study in El | ||
| 342 | Mar Menor (Spain)" | 313 | Mar Menor (Spain)" | ||
| 343 | }, | 314 | }, | ||
| 344 | "topic": | 315 | "topic": | ||
| 345 | "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", | 316 | "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", | ||
| 346 | "type": "dataset", | 317 | "type": "dataset", | ||
| 347 | "url": | 318 | "url": | ||
| 348 | //iepnb.es:443/catalogo/dataset/06ca0bca-0592-5264-b443-8a149b7651c1", | 319 | //iepnb.es:443/catalogo/dataset/06ca0bca-0592-5264-b443-8a149b7651c1", | ||
| 349 | "version_notes": { | 320 | "version_notes": { | ||
| 350 | "es": "" | 321 | "es": "" | ||
| 351 | } | 322 | } | ||
| 352 | } | 323 | } |