A Coupled Index Machine Learning Optimization Framework for River Water Quality Assessment and Control: The Halda River, Bangladesh

The maintenance of a river suitable for fish spawning demands three separate abilities that need to be separately developed: a defendable description of water quality in terms of a scalar parameter, a technique for its economical and continuous estimation, and a rule of converting it into action. This paper integrates the three into a single framework and applies it to the case of the Halda River, the only remaining natural spawning ground for Indian major carps in Bangladesh. The formula of a weighted arithmetic water quality index (WQI) is modified so that the subindex for DO is normalized by the saturation value depending on temperature; the principal term of the resulting index is then equivalent, up to rescaling, to the deficit variable of a Streeter–Phelps oxygen balance, making the index and the load allocation model commensurate in terms of the state variable. Applying the WQI to 18 station-seasons at six stations and three seasons, one obtains: the lower tidal reach at Mohora scores 92.4 (Very poor) during monsoon season and 109.3 (Unsuitable) after monsoon, the impounded reach above the Bhujpur rubber dam remains Excellent (6.3–12.9), and the seasonal average value decreases from 63.6 in monsoon to 34.3 in winter. Seven predictor variables are then learned for predicting the index value without oxygen data using four simple inputs. Extreme randomised tree model with leave-one-out cross validation achieves R² = 0.715 and RMSE = 14.66 index units, logistic regression classifier classifies acceptable and Poor-or-worse water quality states with 100% cross validated accuracy, and the predictive estimator recovers 87.1% of the index variation with 12 out of 13 alert months identified in a 36-month synthetic deployment scenario. The 5000-sample Monte-Carlo simulation analysis shows that the assimilative capacity of Mohora exceed with probability 1.00 in both wet seasons, which requires a 27–43% load reduction. An analytical solution demonstrates that the required reduction is independent of the reaeration-to-deoxygenation rate ratio. Constrained optimisation of aeration and sediment control gives the minimum cost action for Excellent class, and the oxygen rather than the turbidity becomes the binding control.