Matches in SemOpenAlex for { <https://semopenalex.org/work/W4223553247> ?p ?o ?g. }
- W4223553247 endingPage "565" @default.
- W4223553247 startingPage "553" @default.
- W4223553247 abstract "Accurate forecast for water quality is of great importance because it can support water resource management with the future information. In this research, we propose a novel hybrid model by using data decomposition, error correction, and machine learning. In our method, first, the initial forecast is obtained by a prediction model that uses improved complete ensemble empirical mode decomposition with adaptive noise and bidirectional long short-term memory (BLSTM) neural network. Next, a novel error correction framework, which is built by variational mode decomposition and BLSTM neural network, is used to improve forecast accuracy by correcting the initial forecast error. Water quality data of Poyang Lake, China is used to evaluate our model. Results indicate that our model shows highly accurate forecast performance for all of the 9 water quality datasets (the average of mean absolute percentage error (MAPE) of 7 day-ahead forecast is 2.12%; 30 day-ahead forecast is 4.06%). In addition, our model outperforms the competitor models, particularly, compared to the prediction model without error correction framework, the average of MAPE is reduced by 33.33% for 7 day-ahead forecast; 30.48% for 30 day-ahead forecast. This research demonstrates that the proposed error correction framework is an effective tool to improve forecast accuracy for water quality." @default.
- W4223553247 created "2022-04-15" @default.
- W4223553247 creator A5007970044 @default.
- W4223553247 creator A5015810013 @default.
- W4223553247 creator A5019326933 @default.
- W4223553247 creator A5030920461 @default.
- W4223553247 creator A5070910952 @default.
- W4223553247 creator A5087099599 @default.
- W4223553247 date "2022-06-01" @default.
- W4223553247 modified "2023-10-18" @default.
- W4223553247 title "A novel hybrid water quality forecast model based on real-time data decomposition and error correction" @default.
- W4223553247 cites W2000982976 @default.
- W4223553247 cites W2007221293 @default.
- W4223553247 cites W2009465763 @default.
- W4223553247 cites W2011819834 @default.
- W4223553247 cites W2168745915 @default.
- W4223553247 cites W2484979138 @default.
- W4223553247 cites W2501331221 @default.
- W4223553247 cites W2548501426 @default.
- W4223553247 cites W2764126455 @default.
- W4223553247 cites W2811057976 @default.
- W4223553247 cites W2884926439 @default.
- W4223553247 cites W2885097378 @default.
- W4223553247 cites W2897577618 @default.
- W4223553247 cites W2904896091 @default.
- W4223553247 cites W2913364315 @default.
- W4223553247 cites W2915610012 @default.
- W4223553247 cites W2945207508 @default.
- W4223553247 cites W2970366055 @default.
- W4223553247 cites W2990765082 @default.
- W4223553247 cites W2994440657 @default.
- W4223553247 cites W2998062104 @default.
- W4223553247 cites W2998567725 @default.
- W4223553247 cites W2999254773 @default.
- W4223553247 cites W3001710857 @default.
- W4223553247 cites W3005619874 @default.
- W4223553247 cites W3006101764 @default.
- W4223553247 cites W3010537613 @default.
- W4223553247 cites W3015428797 @default.
- W4223553247 cites W3021429973 @default.
- W4223553247 cites W3021915717 @default.
- W4223553247 cites W3030628169 @default.
- W4223553247 cites W3035348935 @default.
- W4223553247 cites W3046452548 @default.
- W4223553247 cites W3047335959 @default.
- W4223553247 cites W3080150692 @default.
- W4223553247 cites W3093988381 @default.
- W4223553247 cites W3095239916 @default.
- W4223553247 cites W3106926559 @default.
- W4223553247 cites W3108741798 @default.
- W4223553247 cites W3109232406 @default.
- W4223553247 cites W3112188931 @default.
- W4223553247 cites W3128009477 @default.
- W4223553247 cites W3128444610 @default.
- W4223553247 cites W3135726705 @default.
- W4223553247 cites W3157049398 @default.
- W4223553247 cites W3159998146 @default.
- W4223553247 cites W3176342080 @default.
- W4223553247 cites W3194960351 @default.
- W4223553247 doi "https://doi.org/10.1016/j.psep.2022.04.020" @default.
- W4223553247 hasPublicationYear "2022" @default.
- W4223553247 type Work @default.
- W4223553247 citedByCount "8" @default.
- W4223553247 countsByYear W42235532472022 @default.
- W4223553247 countsByYear W42235532472023 @default.
- W4223553247 crossrefType "journal-article" @default.
- W4223553247 hasAuthorship W4223553247A5007970044 @default.
- W4223553247 hasAuthorship W4223553247A5015810013 @default.
- W4223553247 hasAuthorship W4223553247A5019326933 @default.
- W4223553247 hasAuthorship W4223553247A5030920461 @default.
- W4223553247 hasAuthorship W4223553247A5070910952 @default.
- W4223553247 hasAuthorship W4223553247A5087099599 @default.
- W4223553247 hasConcept C103088060 @default.
- W4223553247 hasConcept C105795698 @default.
- W4223553247 hasConcept C111919701 @default.
- W4223553247 hasConcept C11413529 @default.
- W4223553247 hasConcept C115961682 @default.
- W4223553247 hasConcept C119857082 @default.
- W4223553247 hasConcept C124101348 @default.
- W4223553247 hasConcept C124681953 @default.
- W4223553247 hasConcept C150217764 @default.
- W4223553247 hasConcept C154945302 @default.
- W4223553247 hasConcept C166851805 @default.
- W4223553247 hasConcept C170061395 @default.
- W4223553247 hasConcept C18903297 @default.
- W4223553247 hasConcept C33923547 @default.
- W4223553247 hasConcept C41008148 @default.
- W4223553247 hasConcept C48677424 @default.
- W4223553247 hasConcept C50644808 @default.
- W4223553247 hasConcept C86803240 @default.
- W4223553247 hasConcept C99498987 @default.
- W4223553247 hasConceptScore W4223553247C103088060 @default.
- W4223553247 hasConceptScore W4223553247C105795698 @default.
- W4223553247 hasConceptScore W4223553247C111919701 @default.
- W4223553247 hasConceptScore W4223553247C11413529 @default.
- W4223553247 hasConceptScore W4223553247C115961682 @default.
- W4223553247 hasConceptScore W4223553247C119857082 @default.
- W4223553247 hasConceptScore W4223553247C124101348 @default.