Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information

Bao Wang; Bin Wang; Wenzhou Wu; Changbai Xi; Jiechen Wang

doi:10.1007/s13131-020-1569-1

Volume 39 Issue 5

May 2020

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Article Navigation > Acta Oceanologica Sinica > 2020 > 39(5): 157-167

Bao Wang, Bin Wang, Wenzhou Wu, Changbai Xi, Jiechen Wang. Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information[J]. Acta Oceanologica Sinica, 2020, 39(5): 157-167. doi: 10.1007/s13131-020-1569-1

Citation:

Bao Wang, Bin Wang, Wenzhou Wu, Changbai Xi, Jiechen Wang. Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information[J]. Acta Oceanologica Sinica, 2020, 39(5): 157-167. doi: 10.1007/s13131-020-1569-1

Citation:

Bao Wang, Bin Wang, Wenzhou Wu, Changbai Xi, Jiechen Wang. Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information[J]. Acta Oceanologica Sinica, 2020, 39(5): 157-167. doi: 10.1007/s13131-020-1569-1

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Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information

doi: 10.1007/s13131-020-1569-1

Bao Wang^{1, 2},
Bin Wang²,
Wenzhou Wu^{3, 4},
Changbai Xi¹,
Jiechen Wang^{1, 5, 6
,
,}

1.
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2.
National Marine Environmental Forecasting Center, Beijing 100081, China
3.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5.
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
6.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing 210023, China

Funds: The National Key R&D Program of China under contract No. 2016YFC1402609.

More Information

Corresponding author: E-mail: wangjiechen@nju.edu.cn
Received Date: 2019-02-08
Accepted Date: 2019-11-19

Available Online: 2020-12-28

Publish Date: 2020-05-25

Abstract

Abstract

Sea-water-level (SWL) prediction significantly impacts human lives and maritime activities in coastal regions, particularly at offshore locations with shallow water levels. Long-term SWL forecasts, which are conventionally obtained via harmonic analysis, become ineffective when nonperiodic meteorological events predominate. Artificial intelligence combined with other data-processing methods can effectively forecast highly nonlinear and nonstationary inflow patterns by recognizing historical relationships between input and output. These techniques are considerably useful in time-series data predictions. This paper reports the development of a hybrid model to realize accurate multihour SWL forecasting by combining an adaptive neuro-fuzzy inference system (ANFIS) with wavelet decomposition while using sea-level anomaly (SLA) and wind-shear-velocity components as inputs. Numerous wavelet-ANFIS (WANFIS) models have been tested using different inputs to assess their applicability as alternatives to the artificial neural network (ANN), wavelet ANN (WANN), and ANFIS models. Different error definitions have been used to evaluate results, which indicate that integrated wavelet-decomposition and ANFIS models improve the accuracy of SWL prediction and that the inputs of SLA and wind-shear velocity exhibit superior prediction capability compared to conventional SWL-only models.
- sea-water level,
- prediction,
- ANFIS,
- wavelet decomposition,
- wind

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References(39)

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