A three-dimensional hydrodynamic-ecological lake model combined with field measurements and sampling was applied to investigate the impacts of floating photovoltaic (PV) systems on hydrodynamics and water quality in a shallow tropical reservoir in Singapore. The model was validated using field data and subsequently applied to predict temperature and water quality changes for a hypothetical 42 ha placement of floating photovoltaic panels, covering about 30% of the water surface and capable of generating up to 50 MW of energy. The impact of the panel placement was studied numerically. The area of the reservoir where panels are placed experiences both light limiting and reduced wind stress conditions. The model indicated an average water temperature increase of 0.3 °C beneath the panels, consistent with the field observation from a 1 ha demonstration installation. Comparisons of model results between the uncovered and covered areas reveal greater stability of the water column (increase in Richardson number from 2.3 to 3.3) and reduction in mixing energy (from 9 × 10^–7 to 7 × 10^–7 W/kg) under the PV panels. Furthermore, the model predicted that chlorophyll a, total organic carbon and dissolved oxygen concentrations would decline by up to 30%, 15% and 50%, respectively, under the photovoltaic panels. Total nitrogen and total phosphorus, averaged over the water column, increased by 10% and 30%, respectively, under the panels. Distant from the floating solar panels, temperature, stability and water quality were unaffected.

Coupled with climate change, the urbanization-driven increase in the frequency and intensity of floods can be seen in both developing and developed countries, and Malaysia is no exemption. As part of flood hazard mitigation, this study aimed to simulate the urban flood scenarios in Malaysia’s urbanized catchments. The flood simulation was performed using the Personal Computer Storm Water Management Model (PCSWMM) modeling of the Damansara catchment as a case study. An integrated hydrologic-hydraulic model was developed for the 1-D river flow modeling and 1-D–2-D drainage overflow modeling. The reliability of the 1-D river flow model was confirmed through the calibration and validation, in which the water level in TTDI Jaya was satisfactorily predicted, supported by the coefficient of determination (R²), Nash–Sutcliffe model efficiency coefficient (NSE), and relative error (RE). The performance of the 1-D–2-D model was further demonstrated based on the flood depth, extent, and risk caused by the drainage overflow. Two scenarios were tested, and the comparison results showed that the current drainage effectively reduced the drainage overflow due to the increased size of drains compared to the historic drainage in 2015. The procedure and findings of this study could serve as references for the application in flood mitigation planning worldwide, especially for developing countries.

The learning algorithms in many of conventional Neuro-Fuzzy Systems (NFS) are based on batch or global learning where all parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, they suffer from a reduced flexibility in their architecture as the number of rules need to be predefined by the user. This study uses a Dynamic Evolving Neural Fuzzy Inference System (DENFIS) in which an evolving, online clustering algorithm, the Evolving Clustering Method (ECM), is implemented. This study focused on evaluating the performance of this model in capturing the rainfall-runoff process and rainfall-water level relationship. The two selected study catchments are located in an urban tropical and in a semi-urbanized area, respectively. The first catchment, Sungai Kayu Ara (23.22 km²), is located in Malaysia, with 10-min rainfall-runoff time-series from which 30 major events are used. The second catchment, Dandenong (272 km²), is located in Victoria, Australia, with daily rainfall and river stage (water level) data from which 11 years of data is used. DENFIS results were then compared with two groups of benchmark models: a regression-based data-driven model known as the Autoregressive Model with Exogenous Inputs (ARX) for both study sites, and physical models Hydrologic Engineering Center–Hydrologic Modelling System (HEC–HMS) and Storm Water Management Model (SWMM) for Sungai Kayu Ara and Dandenong catchments, respectively. DENFIS significantly outperformed the ARX model in both study sites. Moreover, DENFIS was found comparable if not superior to HEC–HMS and SWMM in Sungai Kayu Ara and Dandenong catchments, respectively. A sensitivity analysis was then conducted on DENFIS to assess the impact of training data sequence on its performance. Results showed that starting the training with datasets that include high peaks can improve the model performance. Moreover, datasets with more contrasting values that cover wide range of low to high values can also improve the DENFIS model performance.