Forecasting the sediment load in a river is difficult due to different parameters viz., heavy rainfall and precipitation, tropical climate, transportation of sediment, and so on. The wavelet transformations model helps to analyze the time and frequency information to estimate sediment load by decomposing data over several phases. Inspired from this idea, based on extreme learning machine (ELM) and twin support vector regression (TSVR), this work proposes two coiflet wavelet-based models as, coiflet wavelet-based ELM and coiflet wavelet-based TSVR for sediment load estimation. The results are compared with conventional ELM and TSVR. The performances of the algorithms are examined using five performance evaluation techniques i.e. root mean square error, mean absolute error, ratio between sum of squares error and total sum of squares, symmetric mean absolute percentage error and mean absolute scaled error. The experimental outcomes reveal that the hybrid models based on the coiflet wavelet offer good performance.

中文翻译：

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使用极限学习机和小波联合的双支持向量回归对河流中的悬浮泥沙负荷进行建模

由于不同的参数，即大雨量和降水量，热带气候，沉积物的运输等，很难预测河流中的沉积物负荷。小波变换模型有助于通过分解多个阶段的数据来分析时间和频率信息，以估算泥沙负荷。受此想法的启发，基于极限学习机（ELM）和双支持向量回归（TSVR），这项工作提出了两个基于coiflet小波的模型，即基于coiflet小波的ELM和基于coiflet小波的TSVR用于泥沙负荷估算。将结果与常规ELM和TSVR进行比较。使用五种性能评估技术检查算法的性能，即均方根误差，平均绝对误差，平方和误差与平方和之和，对称平均绝对百分比误差和平均绝对比例误差。实验结果表明，基于coiflet小波的混合模型具有良好的性能。