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.

中文翻译：

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利用SLA和风信息通过小波分解，神经模糊和神经网络相结合的海平面预测

海水水位（SWL）预测会严重影响沿海地区的人类生活和海洋活动，尤其是在浅水位的近海地区。当非周期性气象事件占主导地位时，通常通过谐波分析获得的长期SWL预测将失效。人工智能与其他数据处理方法相结合，可以通过识别输入和输出之间的历史关系来有效预测高度非线性和非平稳的流入方式。这些技术在时间序列数据预测中非常有用。本文报告了一种混合模型的开发，该模型通过将自适应神经模糊推理系统（ANFIS）与小波分解相结合，同时使用海平面异常（SLA）和风切变速度分量作为输入来实现准确的多小时SWL预测。已经使用不同的输入测试了许多小波ANFIS（WANFIS）模型，以评估它们作为人工神经网络（ANN），小波ANN（WANN）和ANFIS模型的替代品的适用性。已使用不同的错误定义来评估结果，这表明集成的小波分解和ANFIS模型提高了SWL预测的准确性，并且与仅使用SWL的传统模型相比，SLA和风切变速度的输入显示出出众的预测能力。已经使用不同的输入测试了许多小波ANFIS（WANFIS）模型，以评估它们作为人工神经网络（ANN），小波ANN（WANN）和ANFIS模型的替代品的适用性。已使用不同的错误定义来评估结果，这表明集成的小波分解和ANFIS模型提高了SWL预测的准确性，并且与仅使用SWL的传统模型相比，SLA和风切变速度的输入显示出出众的预测能力。已经使用不同的输入测试了许多小波ANFIS（WANFIS）模型，以评估它们作为人工神经网络（ANN），小波ANN（WANN）和ANFIS模型的替代品的适用性。已使用不同的错误定义来评估结果，这表明集成的小波分解和ANFIS模型提高了SWL预测的准确性，并且与仅使用SWL的传统模型相比，SLA和风切变速度的输入显示出出众的预测能力。