| Wang Bao,Wang Bin,Wu Wenzhou,Xi Changbai,Wang Jiechen. 2020. Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information. Acta Oceanologica Sinica, 42(5):157－167 |
| Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information |
| 一种基于小波分解和模糊推理神经网络的潮位预测方法 |
| Received:February 08, 2019 |
| DOI：10.1007/s13131-020-1569-1 |
| Key words:sea-water level prediction ANFIS wavelet decomposition wind |
| 中文关键词: 潮位 预测 ANFIS 小波分解 风 |
| 基金项目: |
| Author Name | Affiliation | E-mail | | Wang Bao | School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
National Marine Environmental Forecasting Center, Beijing 100081, China | | | Wang Bin | National Marine Environmental Forecasting Center, Beijing 100081, China | | | Wu Wenzhou | Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China | | | Xi Changbai | School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China | | | Wang Jiechen | School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing 210023, China | wangjiechen@nju.edu.cn |
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| 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. |
| 中文摘要: |
| 潮位预测严重影响沿海区域，尤其是近海浅水沿岸地区居民的生产生活和涉海活动。谐波分析是长周期潮位预测的传统方法，但无法预测非周期性气象过程发生时的水位变化。与数据处理方法相结合，人工智能的方法通过拟合输入与输出数据的历史数值关系，能够有效预测高度非线性和非平稳的流模式，因而在时间序列数据预测领域得到了广泛的应用。本文结合自适应模糊推理系统（Adaptive Neuro-Fuzzy Inference System, ANFIS）和小波分解方法，利用水位异常和风切变分量作为输入数据，实现了一种综合的多时效潮位预测方法。文中测试了多种输入变量组合和小波-ANFIS（WANFIS）模型，并与人工神经网络（Artificial Neural Network, ANN）、小波-ANN（WANN）和ANFIS模型进行了预测结果对比。通过不同指数的误差分析来看，相比ANN模型，ANFIS模型能够更准确的预测潮位变化，小波分解对ANFIS预测精度有一定的提高，且模型中水位异常和风切变分量数据的加入比单一的潮位数据输入能取得更好的预测结果。 |
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