Following the eruption of the Taal Volcano in January 2020 and its continuous signs of unrest in the preceding years, this study delves into the investigation of sediment transport in the Pasig River, Philippines. The historical data of total suspended solids (TSS) and arsenic indicated a notable increase starting from the year 2020. The field measurements were conducted in February and March of 2022, two years after the eruption. Due to the observed homogeneity in the river’s mixing, a refined 1D sediment transport model was developed. In this study, HEC-RAS modeling software was employed. The calibration process using the Laursen transport function yielded an impressive R² value of 0.9989 for the post-eruption model. This predictive accuracy underscores the robustness of the developed model. The study’s scope was further expanded by creating a model for February 2020, incorporating water quality data gathered by the Pasig River Coordinating and Management Office. The model simulation results showed peak TSS values of 120.63 mg/L and 225.15 mg/L in February 2022 and February 2020, respectively. The results of the study highlight the probable impact of geological events on sediment dynamics within the Pasig River, which could help manage and sustain ongoing river improvements.

Excessive nutrients and associated high chlorophyll-a concentration are of growing concern to the public health due to the occurrence of eutrophication in aquatic bodies. Continuous monitoring of chlorophyll-a has limitations, thereby necessitating the development of prediction models. Even though chlorophyll-a prediction models are numerous, they rarely fit into estuarine conditions, which is encountered with dynamic mixing of freshwater and sea water. Thus, the present study identifies the most appropriate driving factors that are relevant for estuarine conditions through chlorophyll-a prediction models developed using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS) approaches in the Ashtamudi estuary, India. Salinity, which is an indirect measure of the estuarine mixing intensity, has been used to replicate the dynamic mixing in the estuary. The results indicate that the model incorporating salinity along with total nitrogen, total phosphorous and turbidity well-predicted chlorophyll-a concentration with a coefficient of determination ranging from 0.73 to 0.99. General Regression Neural Network (GRNN) and ANFIS models outperformed Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models. Of the various ANFIS models, the ones that used Gaussian and Generalized Bell membership functions were most appropriate for the prediction of chlorophyll-a than the models with triangular and trapezoidal membership functions. The study identified that the models that considered salinity were less sensitive and can be used for modelling chlorophyll-a. The chlorophyll-a was observed to have a direct influence of salinity at salinity values greater than 5‰. The study highlighted that estuarine mixing further enhances the primary productivity through increased penetration of light leading to higher chlorophyll-a concentration. Further the study emplasized that the predictive models are important tools fitting well within estuarine environments due to its replicability.