Lq-norm multiple kernel fusion regression for self-cleansing sediment transport

Abstract

Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage and sewer systems. Self-cleansing experiments in the literature are mostly performed on circular channel cross-section, while a few studies considered self-cleansing sediment transport in small rectangular channels. Experiments in this study were carried out in a rectangular channel with a length of 12.5 m, a width of 0.6 m, a depth of 0.7 m and having an automatic control system for regulating channel slope, discharge and sediment rate. Behind utilizing collected experimental data in this study, existing data in the literature for rectangular channels are used to develop self-cleansing models applicable for channel design. Through the modeling procedure, this study recommends Lq-norm multiple kernel fusion regression (LMKFR) techniques for self-cleansing sediment transport. The LMKFR is a regression technique based on the regularized kernel regression method which benefits from the combination of multiple information sources to improve the performance using the Lq-norm multiple kernel learning framework. The results obtained by LMKFR are compared to support vector regression benchmark and existing conventional regression self-cleansing sediment transport models in the literature for rectangular channels. The superiority of LMKFR is illustrated in an accurate modeling as compared with its alternatives in terms of various statistical error measurement criteria. The encouraging results of LMKFR can be linked to utilization of several kernels which are fused effectively using an Lq-norm prior that captures the intrinsic sparsity of the problem at hand. Promising performance of LMKFR technique in this study suggests it as an effective technique to be examined in similar environmental, hydrological and hydraulic problems.