Estimating gabion weir oxygen transfer with data mining

Abstract

Conventionally, impermeable weirs are employed for retaining, measuring, and regulating the water in the river. Today, alternative devices are more predominantly in vogue, which are made of locally available materials called gabion weirs chosen because the latter can better fulfill ecological needs due to their porous nature. Dissolved oxygen (D.O.) is one of the significant determinants for assessing the character of water bodies. This study mainly focuses on improving the estimation of the gabion oxygen transfer efficiency (OTE20) to enhance its efficacy. The backpropagation neural network (BPNN), adaptive neuro-fuzzy inference system (ANFIS), and multi-variant linear and nonlinear regression (MVLR and MVNLR) are developed with experimental data to estimate the OTE20 and their results are compared. In terms of statistical metrics, the BPNN has proved to be the best-performing model. At the same time, triangular membership function (mf)-based ANFIS is the second-best performing model. Nevertheless, other applied mf-based ANFIS, MVLR, and MVNLR are giving a comparable performance. Input variable discharge per unit width (q) is the most crucial parameter in the computation of the OTE20, followed by the gabion mean size (d50). Major challenges are found in computing porosity of the gabion materials and optimal parameters of proposed data mining techniques. HIGHLIGHTS Gabion weir oxygen transfer efficiency is studied in the laboratory.; Data-driven and conventional models are developed.; The BPNN is found to be the best model, nevertheless other proposed models are giving comparable results.; The discharge per unit width is the most sensitive input variable.;

Keywords

• backpropagation neural network
• data mining
• gabion weir oxygen aeration efficiency
• neuro-fuzzy
• sensitivity investigation