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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

Author:chandra sekhar matli <> (national institute of technology)
Presenter:chandra sekhar matli <> (national institute of technology)
Date: 2006-06-18     Track: General Sessions     Session: General

The Krishna river is subjected to a varying degree of pollution, caused by numerous outfalls - municipal and industrial effluents and by other human activities. The main sources leading to pollution in the river include municipal wastewater from urban areas of Hyderabad and Nalgonda, and wastewater from a variety of industries. Non-point runoff is generated by precipitation that washes and cleanses the air and land surface and then transports, a variety of materials, such as sediment, animal wastes, fertilizers and leaves, to the nearest natural or man made collection channel. Hence, it is becoming increasingly evident, that to establish the goals of the water quality management programme, regulating and controlling only point source pollution is not sufficient. In addition to the point sources, considerable pollution reaches the river from various land use activities during the monsoon period. Runoff from the agricultural lands, unsewered rural and urban areas, etc., is the source of non-point source pollution in this part of the basin. Seasonal variations in quantity and quality of Krishna river water are significant due to non-uniform distribution of rainfall and hence the discharge in the river. During the dry season the river water is polluted due to discharge of treated/partially treated/untreated domestic and industrial wastewaters. In wet season, the river receives pollutants from non-point sources in addition to pollutants from point sources. As the flow variations in the river are very large, the flows are classified as wet flows (June - November) and dry flows (December - May) for developing regression equations. A generalized regression equation which can be used for water quality predictions did not yield in good predictions. To incorporate the influence of previous flow on the present load of the pollutant, ANFIS models are developed. Also, to develop generalized models for the river stretch, Fuzzy Inference System is used for the first time and its applicability is tested. Using concepts of fuzzy sets the flows are classified as low and high. The influence of previous flow on the present pollutant load could be incorporated by giving suitable weights by the ANFIS model. Application of ANFIS for developing a generalized model for the river reach under study yielded good results. The correlation coefficients in the range of 0.6 to 0.9 and low RMSE indicate suitability of the models to the study area. The models successfully explained the variation in loads for different flow conditions in the river. The peak flow and falling stage conditions indicated the influence of previous flow on the load due to delayed effect. The models are verified using linear regression. Agreement between model predictions and observed measurements is within the uncertainty of data. However, for all the pollutants the observed values fall within 95% confidence bands.