Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin.

The effectiveness of annual peak discharges under the anthropogenic impact and climate change has significance for disaster management and planning. Therefore, an attempt has been made to study the trend of annual maximum series (AMS) discharges and flood frequency in the Lower Mekong Basin (LMB). The AMS data of five stations in the LMB were procured from the Mekong River Commission for analyses of trends of the AMS and flood frequency.

Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence.

Predicting the bulk-average velocity (U) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict U and the friction factor in the surface layer (f) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost).

Development of river water quality indices-a review.

The use of water quality indices (WQIs) as a tool to evaluate the status of water quality in rivers has been introduced since the 1960s. The WQI transforms selected water quality parameters into a dimensionless number so that changes in river water quality at any particular location and time could be presented in a simple and easily understandable manner. Although many WQIs have been developed, there is no worldwide accepted method for implementing the steps used for developing a WQI.

Prediction of algal blooms using genetic programming.

In this study, an attempt was made to mathematically model and predict algal blooms in Tolo Harbor (Hong Kong) using genetic programming (GP). Chlorophyll plays a vital role in blooms and was used in this model as a measure of algal bloom biomass, and eight other variables were used as input for its prediction. It has been observed that GP evolves multiple models with almost the same values of errors-of-measure.