التفاصيل البيبلوغرافية
| العنوان: |
A multi-model ensemble approach for reservoir dissolved oxygen forecasting based on feature screening and machine learning |
| المؤلفون: |
Peng Zhang, Xinyang Liu, Huancheng Dai, Chengchun Shi, Rongrong Xie, Gangfu Song, Lei Tang |
| المصدر: |
Ecological Indicators, Vol 166, Iss , Pp 112413- (2024) |
| بيانات النشر: |
Elsevier, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Ecology |
| مصطلحات موضوعية: |
Dissolved oxygen (DO), Hypoxia prediction, Maximum information coefficient (MIC), Machine learning, Ensemble learning, Ecology, QH540-549.5 |
| الوصف: |
Dissolved oxygen (DO) concentration in aquatic systems plays a vital role in water aquaculture. An innovative approach that combines feature selection and ensemble learning to predict DO in aquatic ecosystems was proposed. Feature selection was first performed using Maximum Information Coefficient (MIC). Five machine learning algorithms were then employed to construct five hybrid-MIC models, including K-Nearest Neighbors (KNN), Backpropagation (BP) Neural Network, Long Short-Term Memory (LSTM), Kernel Ridge Regression (KRR), and Support Vector Regression (SVR). Finally, an ensemble-RF prediction model was built using Random Forests(RF). The main findings are as follows: (1) The MIC technique can effectively identify the key factors influencing DO. (2) The MIC significantly improves model performance. (3) The hybrid-MIC model was further improved by the ensemble-RF model, the average R2 and NSE were both as high as 0.99, and the average MAE and RMSE were decreased by 72 % and 64 %, respectively. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1470-160X |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1470160X24008707; https://doaj.org/toc/1470-160X |
| DOI: |
10.1016/j.ecolind.2024.112413 |
| URL الوصول: |
https://doaj.org/article/c0fa366f2ee746858becaec298375181 |
| رقم الانضمام: |
edsdoj.0fa366f2ee746858becaec298375181 |
| قاعدة البيانات: |
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