Flow Forecasting and Measuring Environmental Flow Using Machine Learning Techniques

Abstract

Flow alteration study in rivers persuaded by damming is well studied and reported significant flow failure. But how far the existing flow is ecologically ambient, in near future and what will be such situation are less addressed. Particularly, no such work is available in Atreyee river, a transboundary river between India and Bangladesh, the present area of interest. But it is essential from socio-ecological and ecosystem standpoints. Considering this, the present study has tried to predict river flow and assess its ecological relevance using soft computing advanced machine learning techniques like ANN, SVM and RF. Eco-deficit and surplus were assessed using a flow duration curve (FDC). The results indicate all the months' recorded eco-deficit conditions. Estimated environmental flow is moderately modified at present as per Environmental Management Class (EMC). The observed flow is 37.8m3/sec which is less than FDC estimated environmental flow (42.3m3/sec). Predicted flow in 2028, as per the best suited ANN, would be 56% lesser than present indicating the further departure of flow from the required environmental flow. It will perhaps pose an excessive burden to the ecosystem and socio-ecological fabrics.