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  "author": "Jimeno-Saez, P., Senent-Aparicio, J., Cecilia, J.M. y 
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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 
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environmental value and a characteristic example of a highly 
            
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anthropized hydro-ecosystem located in the southeast of Spain. An 
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anthropized hydro-ecosystem located in the southeast of Spain. An 
            
102
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unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
            
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113
changes in the quality of its waters caused a great social alarm. 
            
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114
Understanding and modeling the level of a eutrophication indicator, 
            
105
such as chlorophyll-a (Chl-a), benefits the management of this complex 
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such as chlorophyll-a (Chl-a), benefits the management of this complex 
            
106
system. In this study, we investigate the potential machine learning 
116
system. In this study, we investigate the potential machine learning 
            
107
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
117
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
            
108
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
118
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
            
109
evaluated using as a target dataset information of up to nine 
119
evaluated using as a target dataset information of up to nine 
            
110
different water quality parameters. The most relevant input 
120
different water quality parameters. The most relevant input 
            
111
combinations were extracted using wrapper feature selection methods 
121
combinations were extracted using wrapper feature selection methods 
            
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which simplified the structure of the model, resulting in a more 
122
which simplified the structure of the model, resulting in a more 
            
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accurate and efficient procedure. Although the performance in the 
123
accurate and efficient procedure. Although the performance in the 
            
114
validation phase showed that SVR models obtained better results than 
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validation phase showed that SVR models obtained better results than 
            
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MLNNs, experimental results indicated that both ML algorithms provide 
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MLNNs, experimental results indicated that both ML algorithms provide 
            
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satisfactory results in the prediction of Chl-a concentration, 
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satisfactory results in the prediction of Chl-a concentration, 
            
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reaching up to 0.7 R-CV(2) (cross-validated coefficient of 
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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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    "es": "The Mar Menor is a hypersaline coastal lagoon with high 
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    "es": "The Mar Menor is a hypersaline coastal lagoon with high 
            
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environmental value and a characteristic example of a highly 
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environmental value and a characteristic example of a highly 
            
122
anthropized hydro-ecosystem located in the southeast of Spain. An 
132
anthropized hydro-ecosystem located in the southeast of Spain. An 
            
123
unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
133
unprecedented eutrophication crisis in 2016 and 2019 with abrupt 
            
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changes in the quality of its waters caused a great social alarm. 
134
changes in the quality of its waters caused a great social alarm. 
            
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Understanding and modeling the level of a eutrophication indicator, 
135
Understanding and modeling the level of a eutrophication indicator, 
            
126
such as chlorophyll-a (Chl-a), benefits the management of this complex 
136
such as chlorophyll-a (Chl-a), benefits the management of this complex 
            
127
system. In this study, we investigate the potential machine learning 
137
system. In this study, we investigate the potential machine learning 
            
128
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
138
(ML) methods to predict the level of Chl-a. Particularly, Multilayer 
            
129
Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
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Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are 
            
130
evaluated using as a target dataset information of up to nine 
140
evaluated using as a target dataset information of up to nine 
            
131
different water quality parameters. The most relevant input 
141
different water quality parameters. The most relevant input 
            
132
combinations were extracted using wrapper feature selection methods 
142
combinations were extracted using wrapper feature selection methods 
            
133
which simplified the structure of the model, resulting in a more 
143
which simplified the structure of the model, resulting in a more 
            
134
accurate and efficient procedure. Although the performance in the 
144
accurate and efficient procedure. Although the performance in the 
            
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validation phase showed that SVR models obtained better results than 
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validation phase showed that SVR models obtained better results than 
            
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MLNNs, experimental results indicated that both ML algorithms provide 
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MLNNs, experimental results indicated that both ML algorithms provide 
            
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satisfactory results in the prediction of Chl-a concentration, 
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satisfactory results in the prediction of Chl-a concentration, 
            
138
reaching up to 0.7 R-CV(2) (cross-validated coefficient of 
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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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