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. The Mann-Kendall test showed a significant decrease in the magnitude of annual peak floods for all the discharge gauging sites in the LMB. Likewise, the analysis of the annual discharge departure from the mean reveals noteworthy variations and departure (positive and negative) in the annual peak discharges. The goodness-of-fit (GoF) tests showed that Log-Pearson Type-III (LP-III) is the best distribution for AMS of the Mekong River than Gumbel Extreme Value Type-I (GEVI). Therefore, predicted discharges for different return periods and predicted recurrence intervals for average annual discharges (Qm), large floods (Qlf), and maximum annual peak discharge during the recording period (Qmax) by LP-III are trustworthy. The flood frequency curve specified that all the observed discharges were fairly on the best-fitted line and falls between upper and lower confidence limits. Inclusively, the results of the trend in annual peak discharges and flood frequency are consistent and can be used for water management, controlling flood disasters, and flood planning in the LMB.

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 (UB) 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 UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions.

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. Thus, there is a continuing interest to develop accurate WQIs that suit a local or regional area. This paper aimed to provide significant contribution to the development of future river WQIs through a review of 30 existing WQIs based on the four steps needed to develop a WQI. These steps are the selection of parameters, the generation of sub-indices, the generation of parameter weights and the aggregation process to compute the final index value. From the 30 reviewed WQIs, 7 were identified as most important based on their wider use and they were discussed in detail. It was observed that a major factor that influences wider use of a WQI is the support provided by the government and authorities to implement a WQI as the main tool to evaluate the status of rivers. Since there is a lot of subjectivity and uncertainty involved in the steps for developing and applying a WQI, it is recommended that the opinion of local water quality experts is taken, especially in the first three steps (through techniques like Delphi method). It was also observed that uncertainty and sensitivity analysis was rarely undertaken to reduce uncertainty, and hence such an analysis is recommended for future studies.

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. Previous studies on GP modeling have primarily focused on comparing GP results with actual values. In contrast, in this study, the main aim was to propose a systematic procedure for identifying the most appropriate GP model from a list of feasible models (with similar error-of-measure) using a physical understanding of the process aided by data interpretation. Evaluation of the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of the final GP-evolved mathematical model indicates that, of the eight variables assumed to affect algal blooms, the most significant effects are due to chlorophyll, total inorganic nitrogen and dissolved oxygen for a 1-week prediction. For longer lead predictions (biweekly), secchi-disc depth and temperature appear to be significant variables, in addition to chlorophyll.