Discriminant analysis for the prediction of sand mass distribution in a holding pond using deposition thickness model of a single grain-sized particle

Abstract

Discriminant analysis (DA) was applied to derive the classification function to spatially determine the sand mass distribution in an urban stormwater holding pond, Malaysia, using simulated deposition thickness results of a single grain-sized particle (d₅₀ = 0.375 mm). The upstream boundary lines at the inflow gates of the hydrodynamic model were fixed to three inflow parameter values, 16, 40 and 80 m³ s⁻¹, representing the inflow variations of diverted floodwater into the holding pond during major storm events. The annual mean suspended sediment concentration of approximately 373 mg l⁻¹ at the confluence of the Klang and Ampang Rivers was fixed as input parameter value for the sand deposition model. The simulated deposition thickness of the single grain-sized particle displays a significantly strong correlation with the measured sand mass content of the surface sediment samples at corresponding locations. The surface sediment sampling locations were hierarchically clustered into two groups based on the sand mass composition of the sediment samples. Group one comprises nine sampling locations representing areas of relatively higher sand mass composition ranging from 16.9 to 45.5 %. Group two comprises twenty sampling locations representing areas of relatively lower sand mass composition ranging from 3.7 to 12.2 %. DA was conducted to generate linear combination of the independent variables that will discriminate best between the objects in the groups. Two classification functions also known as Fisher’s linear discriminant functions, F ₁ = −6.441 − 125.438 × A + 57.519 × B and F ₂ = −1.248 − 53.442 × A + 24.424 × B, were produced to predict group memberships of relatively high and low sand mass composition areas, where A and B denote the simulated deposition thickness results of a single grain-sized particle computed at nodal points of the model domain, generated by an inflow magnitude of 16 and 40 m³ s⁻¹, respectively. The classification of areas which comprises of relatively high and low sand mass composition is based on whichever group (F ₁ or F ₂) is related to the highest classification score. Classification result indicates that the model correctly predicts 66.7 and 100.0 % of the surface sediment sampling locations consisting of relatively high (group one) and low (group two) sand mass percentage, respectively. The predicted results of the sand mass distribution show that 23.6 % of the model domain area (25.20 m²) at the western bank region consists of a relatively higher sand mass content compared to the remaining 76.4 % comprising areas of lower sand mass content mostly situated at the central and south-eastern regions of the holding pond. The predicted sand mass distribution results can be used implicitly to spatially distinguish the most likely contaminated sediment areas and identify accurately sampling locations of polluted sediment for environmental impact assessment study prior conducting any dredging activities for future maintenance.