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en A new approach to monitor water quality in the Menor Sea (Spain) using satellite data and machine learning methods -
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) en A new approach to monitor water quality in the Menor Sea (Spain) using satellite data and machine learning methods
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| 89 | "notes": "The Menor sea is a coastal lagoon declared by the European | 99 | "notes": "The Menor sea is a coastal lagoon declared by the European | ||
| 90 | Union as a sensitive area to eutrophication due to human activities. | 100 | Union as a sensitive area to eutrophication due to human activities. | ||
| 91 | To control the deterioration of its water quality, it is necessary to | 101 | To control the deterioration of its water quality, it is necessary to | ||
| 92 | monitor some parameters such as chlorophyll-a (chl-a), which indicates | 102 | monitor some parameters such as chlorophyll-a (chl-a), which indicates | ||
| 93 | phytoplankton biomass in the water. In the study area, current efforts | 103 | phytoplankton biomass in the water. In the study area, current efforts | ||
| 94 | focus on in-situ measurements to estimate chl-a by means of a few | 104 | focus on in-situ measurements to estimate chl-a by means of a few | ||
| 95 | permanent stations and seasonal oceanographic campaigns, however they | 105 | permanent stations and seasonal oceanographic campaigns, however they | ||
| 96 | are expensive and time consuming. In this work, we proposed a machine | 106 | are expensive and time consuming. In this work, we proposed a machine | ||
| 97 | learning approach based on Sentinel-2 data to estimate chl-a content | 107 | learning approach based on Sentinel-2 data to estimate chl-a content | ||
| 98 | on the upper part of the water column. Random forest (rf), support | 108 | on the upper part of the water column. Random forest (rf), support | ||
| 99 | vector machine (svmRadial), Artificial Neural Network (ANN) and Deep | 109 | vector machine (svmRadial), Artificial Neural Network (ANN) and Deep | ||
| 100 | Neural Network (DNN) algorithms were utilized under three feature | 110 | Neural Network (DNN) algorithms were utilized under three feature | ||
| 101 | selection scenarios, and several spectral indices were used in | 111 | selection scenarios, and several spectral indices were used in | ||
| 102 | combination with Sentinel 2 bands. Rf, svmRadial and DNN performed | 112 | combination with Sentinel 2 bands. Rf, svmRadial and DNN performed | ||
| 103 | better when all the available predictors were included in the models | 113 | better when all the available predictors were included in the models | ||
| 104 | (RMSE = 0.82, 0.82 and 1.76 mg/m3 respectively), whereas ANN achieved | 114 | (RMSE = 0.82, 0.82 and 1.76 mg/m3 respectively), whereas ANN achieved | ||
| 105 | better results under scenario c (principal components). Our results | 115 | better results under scenario c (principal components). Our results | ||
| 106 | demonstrate the possibility to estimate chl-a concentration in a | 116 | demonstrate the possibility to estimate chl-a concentration in a | ||
| 107 | cost-effective manner and thereby provide near-real time information | 117 | cost-effective manner and thereby provide near-real time information | ||
| 108 | to monitor the water quality of the Menor sea, what can be of great | 118 | to monitor the water quality of the Menor sea, what can be of great | ||
| 109 | interest for local authorities, tourism and fishing industry.", | 119 | interest for local authorities, tourism and fishing industry.", | ||
| 110 | "notes_translated": { | 120 | "notes_translated": { | ||
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| 112 | Union as a sensitive area to eutrophication due to human activities. | 122 | Union as a sensitive area to eutrophication due to human activities. | ||
| 113 | To control the deterioration of its water quality, it is necessary to | 123 | To control the deterioration of its water quality, it is necessary to | ||
| 114 | monitor some parameters such as chlorophyll-a (chl-a), which indicates | 124 | monitor some parameters such as chlorophyll-a (chl-a), which indicates | ||
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| 116 | focus on in-situ measurements to estimate chl-a by means of a few | 126 | focus on in-situ measurements to estimate chl-a by means of a few | ||
| 117 | permanent stations and seasonal oceanographic campaigns, however they | 127 | permanent stations and seasonal oceanographic campaigns, however they | ||
| 118 | are expensive and time consuming. In this work, we proposed a machine | 128 | are expensive and time consuming. In this work, we proposed a machine | ||
| 119 | learning approach based on Sentinel-2 data to estimate chl-a content | 129 | learning approach based on Sentinel-2 data to estimate chl-a content | ||
| 120 | on the upper part of the water column. Random forest (rf), support | 130 | on the upper part of the water column. Random forest (rf), support | ||
| 121 | vector machine (svmRadial), Artificial Neural Network (ANN) and Deep | 131 | vector machine (svmRadial), Artificial Neural Network (ANN) and Deep | ||
| 122 | Neural Network (DNN) algorithms were utilized under three feature | 132 | Neural Network (DNN) algorithms were utilized under three feature | ||
| 123 | selection scenarios, and several spectral indices were used in | 133 | selection scenarios, and several spectral indices were used in | ||
| 124 | combination with Sentinel 2 bands. Rf, svmRadial and DNN performed | 134 | combination with Sentinel 2 bands. Rf, svmRadial and DNN performed | ||
| 125 | better when all the available predictors were included in the models | 135 | better when all the available predictors were included in the models | ||
| 126 | (RMSE = 0.82, 0.82 and 1.76 mg/m3 respectively), whereas ANN achieved | 136 | (RMSE = 0.82, 0.82 and 1.76 mg/m3 respectively), whereas ANN achieved | ||
| 127 | better results under scenario c (principal components). Our results | 137 | better results under scenario c (principal components). Our results | ||
| 128 | demonstrate the possibility to estimate chl-a concentration in a | 138 | demonstrate the possibility to estimate chl-a concentration in a | ||
| 129 | cost-effective manner and thereby provide near-real time information | 139 | cost-effective manner and thereby provide near-real time information | ||
| 130 | to monitor the water quality of the Menor sea, what can be of great | 140 | to monitor the water quality of the Menor sea, what can be of great | ||
| 131 | interest for local authorities, tourism and fishing industry." | 141 | interest for local authorities, tourism and fishing industry." | ||
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| 227 | 37.38]]]}", | 237 | 37.38]]]}", | ||
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| 235 | "text": "Regi\u00f3n de Murcia", | 245 | "text": "Regi\u00f3n de Murcia", | ||
| 236 | "uri": | 246 | "uri": | ||
| 237 | atos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia" | 247 | atos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia" | ||
| 238 | } | 248 | } | ||
| 239 | ], | 249 | ], | ||
| 240 | "spatial_uri": | 250 | "spatial_uri": | ||
| 241 | tos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia", | 251 | tos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia", | ||
| 242 | "state": "active", | 252 | "state": "active", | ||
| 243 | "study_variables": { | 253 | "study_variables": { | ||
| 244 | "es": "" | 254 | "es": "" | ||
| 245 | }, | 255 | }, | ||
| 246 | "tag_uri": [ | 256 | "tag_uri": [ | ||
| 247 | 257 | ||||
| 248 | tp://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment" | 258 | tp://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment" | ||
| 249 | ], | 259 | ], | ||
| 250 | "tags": [ | 260 | "tags": [ | ||
| 251 | { | 261 | { | ||
| 252 | "display_name": "conservacion_in_situ", | 262 | "display_name": "conservacion_in_situ", | ||
| 253 | "id": "8edcf888-3e32-4fea-8193-1ec274261390", | 263 | "id": "8edcf888-3e32-4fea-8193-1ec274261390", | ||
| 254 | "name": "conservacion_in_situ", | 264 | "name": "conservacion_in_situ", | ||
| 255 | "state": "active", | 265 | "state": "active", | ||
| 256 | "vocabulary_id": null | 266 | "vocabulary_id": null | ||
| 257 | }, | 267 | }, | ||
| 258 | { | 268 | { | ||
| 259 | "display_name": "lagunas-costeras-salobres-saladas", | 269 | "display_name": "lagunas-costeras-salobres-saladas", | ||
| 260 | "id": "92dd4d50-2752-4df9-8eeb-64128064bda8", | 270 | "id": "92dd4d50-2752-4df9-8eeb-64128064bda8", | ||
| 261 | "name": "lagunas-costeras-salobres-saladas", | 271 | "name": "lagunas-costeras-salobres-saladas", | ||
| 262 | "state": "active", | 272 | "state": "active", | ||
| 263 | "vocabulary_id": null | 273 | "vocabulary_id": null | ||
| 264 | }, | 274 | }, | ||
| 265 | { | 275 | { | ||
| 266 | "display_name": "marino", | 276 | "display_name": "marino", | ||
| 267 | "id": "004084d1-68d8-46ca-842a-c10278540bc9", | 277 | "id": "004084d1-68d8-46ca-842a-c10278540bc9", | ||
| 268 | "name": "marino", | 278 | "name": "marino", | ||
| 269 | "state": "active", | 279 | "state": "active", | ||
| 270 | "vocabulary_id": null | 280 | "vocabulary_id": null | ||
| 271 | }, | 281 | }, | ||
| 272 | { | 282 | { | ||
| 273 | "display_name": "seguimiento", | 283 | "display_name": "seguimiento", | ||
| 274 | "id": "35753cbf-b84a-487f-80a1-3930d85a73a0", | 284 | "id": "35753cbf-b84a-487f-80a1-3930d85a73a0", | ||
| 275 | "name": "seguimiento", | 285 | "name": "seguimiento", | ||
| 276 | "state": "active", | 286 | "state": "active", | ||
| 277 | "vocabulary_id": null | 287 | "vocabulary_id": null | ||
| 278 | }, | 288 | }, | ||
| 279 | { | 289 | { | ||
| 280 | "display_name": "tecnologia", | 290 | "display_name": "tecnologia", | ||
| 281 | "id": "0b3381bc-073c-43fe-a133-4efcf0e36300", | 291 | "id": "0b3381bc-073c-43fe-a133-4efcf0e36300", | ||
| 282 | "name": "tecnologia", | 292 | "name": "tecnologia", | ||
| 283 | "state": "active", | 293 | "state": "active", | ||
| 284 | "vocabulary_id": null | 294 | "vocabulary_id": null | ||
| 285 | } | 295 | } | ||
| 286 | ], | 296 | ], | ||
| 287 | "thematic_area": [ | 297 | "thematic_area": [ | ||
| 288 | "espacios_protegidos" | 298 | "espacios_protegidos" | ||
| 289 | ], | 299 | ], | ||
| 290 | "theme_es": [ | 300 | "theme_es": [ | ||
| 291 | "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente" | 301 | "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente" | ||
| 292 | ], | 302 | ], | ||
| 293 | "title": "A new approach to monitor water quality in the Menor Sea | 303 | "title": "A new approach to monitor water quality in the Menor Sea | ||
| 294 | (Spain) using satellite data and machine learning methods", | 304 | (Spain) using satellite data and machine learning methods", | ||
| 295 | "title_translated": { | 305 | "title_translated": { | ||
| 296 | "es": "A new approach to monitor water quality in the Menor Sea | 306 | "es": "A new approach to monitor water quality in the Menor Sea | ||
| 297 | (Spain) using satellite data and machine learning methods" | 307 | (Spain) using satellite data and machine learning methods" | ||
| 298 | }, | 308 | }, | ||
| 299 | "topic": | 309 | "topic": | ||
| 300 | "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", | 310 | "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", | ||
| 301 | "type": "dataset", | 311 | "type": "dataset", | ||
| 302 | "url": | 312 | "url": | ||
| 303 | //iepnb.es:443/catalogo/dataset/d8ae6b28-75a0-51e8-abf6-ae292c4fbe5d", | 313 | //iepnb.es:443/catalogo/dataset/d8ae6b28-75a0-51e8-abf6-ae292c4fbe5d", | ||
| 304 | "version_notes": { | 314 | "version_notes": { | ||
| 305 | "es": "" | 315 | "es": "" | ||
| 306 | } | 316 | } | ||
| 307 | } | 317 | } |