Evaluation of management scenarios for controlling eutrophication in a shallow tropical urban lake

Urban lakes are typically smaller, shallower, and more exposed to human activities than natural lakes. Although the effects of harmful algal blooms (HABs) associated with eutrophication in urban lakes has become a growing concern for water resources management and environmental protection, studies focussing on this topic in relation to urban lakes are rare and knowledge of the ecological dynamics and effective management strategies for controlling eutrophication in urban lakes is lacking. This study applied an integrated three-dimensional hydrodynamics-ecological model for a small shallow tropical urban lake in Singapore and evaluated various management scenarios to control eutrophication in the lake. It is found that in-lake treatment techniques including artificial destratification, sediment manipulation and algaecide addition are either ineffective or possess environmental concerns; while watershed management strategies including hydraulic flushing and inflow nutrients reduction are more effective and have posed less environmental concerns. In this study, inflow phosphorus reduction was found to be the best strategy after evaluating the advantages and drawbacks of the management strategies studied. Runoff from the watershed exerts significant influence on urban lakes and thus an integrated water resources management at the watershed level is critical for the control of eutrophicatio

Hybrid neural network—finite element river flow model

Results obtained from a hybrid neural network—finite element model are reported in this paper. The hybrid model incorporates artificial neural network (ANN) nodes into a numerical scheme, which solves the two-dimensional shallow water equations using finite elements (FE). First, numerical computations are carried out on the entire numerical model, using a larger mesh. The results from this computation are then used to train several preselected ANN nodes. The ANN nodes model the response for a part of the entire numerical model by transferring the system reaction to the location where both models are connected in real time. This allows a smaller mesh to be used in the hybrid ANN-FE model, resulting in savings in computation time. The hybrid model was developed for a river application, using the computational nodes located at the open boundaries to be the ANN nodes for the ANN-FE hybrid model. Real-time coupling between the ANN and FE models was achieved, and a reduction is CPU time of more than 25% was obtained

Influence of lag time on event-based rainfall–runoff modeling using the data driven approach

This study investigated the effect of lag time on the performance of data-driven models, specifically the adaptive network-based fuzzy inference system (ANFIS), in event-based rainfall–runoff modeling. Rainfall and runoff data for a catchment in Singapore were chosen for this study. For the purpose of this study, lag time was determined from cross-correlation analysis of the rainfall and runoff time series. Rainfall antecedents were the only inputs of the models and direct runoff was the desired output. An ANFIS model with three sub-models defined based on three different ranges of lag times was developed. The performance of the sub-models was compared with previously developed ANFIS models and the physicallybased Storm Water Management Model (SWMM). The ANFIS sub-models gave significantly superior results in terms of the RMSE, r2, CE and the prediction of the peak discharge, compared to other ANFIS models where the lag time was not considered. In addition, the ANFIS sub-models provided results that were comparable with results from SWMM. It is thus concluded that the lag time plays an important role in the selection of events for training and testing of data-driven models in event-based rainfall–runoff modeling