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  "author": "Jimeno-Saez, P., Senent-Aparicio, J., Cecilia, J.M. y 
            
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Perez-Sanchez, J.",
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  "author_name": "Jimeno-Saez, P., Senent-Aparicio, J., Cecilia, J.M. 
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  "notes": "The Mar Menor is a hypersaline coastal lagoon with high 
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  "notes": "The Mar Menor is a hypersaline coastal lagoon with high 
            
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environmental value and a characteristic example of a highly 
100
environmental value and a characteristic example of a highly 
            
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anthropized hydro-ecosystem located in the southeast of Spain. An 
101
anthropized hydro-ecosystem located in the southeast of Spain. An 
            
102
unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
102
unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
            
103
changes in the quality of its waters caused a great social alarm. 
103
changes in the quality of its waters caused a great social alarm. 
            
104
Understanding and modeling the level of a eutrophication indicator, 
104
Understanding and modeling the level of a eutrophication indicator, 
            
105
such as chlorophyll-a (Chl-a), benefits the management of this complex 
105
such as chlorophyll-a (Chl-a), benefits the management of this complex 
            
106
system. In this study, we investigate the potential machine learning 
106
system. In this study, we investigate the potential machine learning 
            
107
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
107
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
            
108
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
108
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
            
109
evaluated using as a target dataset information of up to nine 
109
evaluated using as a target dataset information of up to nine 
            
110
different water quality parameters. The most relevant input 
110
different water quality parameters. The most relevant input 
            
111
combinations were extracted using wrapper feature selection methods 
111
combinations were extracted using wrapper feature selection methods 
            
112
which simplified the structure of the model, resulting in a more 
112
which simplified the structure of the model, resulting in a more 
            
113
accurate and efficient procedure. Although the performance in the 
113
accurate and efficient procedure. Although the performance in the 
            
114
validation phase showed that SVR models obtained better results than 
114
validation phase showed that SVR models obtained better results than 
            
115
MLNNs, experimental results indicated that both ML algorithms provide 
115
MLNNs, experimental results indicated that both ML algorithms provide 
            
116
satisfactory results in the prediction of Chl-a concentration, 
116
satisfactory results in the prediction of Chl-a concentration, 
            
117
reaching up to 0.7 R-CV(2) (cross-validated coefficient of 
117
reaching up to 0.7 R-CV(2) (cross-validated coefficient of 
            
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determination) for the best-fit models.",
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determination) for the best-fit models.",
            
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  "notes_translated": {
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    "es": "The Mar Menor is a hypersaline coastal lagoon with high 
120
    "es": "The Mar Menor is a hypersaline coastal lagoon with high 
            
121
environmental value and a characteristic example of a highly 
121
environmental value and a characteristic example of a highly 
            
122
anthropized hydro-ecosystem located in the southeast of Spain. An 
122
anthropized hydro-ecosystem located in the southeast of Spain. An 
            
123
unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
123
unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
            
124
changes in the quality of its waters caused a great social alarm. 
124
changes in the quality of its waters caused a great social alarm. 
            
125
Understanding and modeling the level of a eutrophication indicator, 
125
Understanding and modeling the level of a eutrophication indicator, 
            
126
such as chlorophyll-a (Chl-a), benefits the management of this complex 
126
such as chlorophyll-a (Chl-a), benefits the management of this complex 
            
127
system. In this study, we investigate the potential machine learning 
127
system. In this study, we investigate the potential machine learning 
            
128
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
128
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
            
129
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
129
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
            
130
evaluated using as a target dataset information of up to nine 
130
evaluated using as a target dataset information of up to nine 
            
131
different water quality parameters. The most relevant input 
131
different water quality parameters. The most relevant input 
            
132
combinations were extracted using wrapper feature selection methods 
132
combinations were extracted using wrapper feature selection methods 
            
133
which simplified the structure of the model, resulting in a more 
133
which simplified the structure of the model, resulting in a more 
            
134
accurate and efficient procedure. Although the performance in the 
134
accurate and efficient procedure. Although the performance in the 
            
135
validation phase showed that SVR models obtained better results than 
135
validation phase showed that SVR models obtained better results than 
            
136
MLNNs, experimental results indicated that both ML algorithms provide 
136
MLNNs, experimental results indicated that both ML algorithms provide 
            
137
satisfactory results in the prediction of Chl-a concentration, 
137
satisfactory results in the prediction of Chl-a concentration, 
            
138
reaching up to 0.7 R-CV(2) (cross-validated coefficient of 
138
reaching up to 0.7 R-CV(2) (cross-validated coefficient of 
            
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determination) for the best-fit models."
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determination) for the best-fit models."
            
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