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Highlights • The paper deals with two different water quality indicators for the Bakreswar reservoir. • The main indicators chosen here are dissolved oxygen and zooplankton abundance. • Artificial neural network modelling has been used to make predictions of DO and zooplankton abundance. • ANN are capable of accurately predicting the dependent variable based on predictor variables. • ANN modelling is better suited in the current scenario over traditional approaches like multiple regression. Abstract Presence of optimal levels of dissolved oxygen (above critical level of 4.5 mg L−1) and presence of zooplankton community are indicators of good water quality of an aquatic ecosystem and also of the health of the same. Reservoirs being artificially created water bodies present hybrid systems containing features of both lotic and lentic systems and thus have unique organization that are representative of both rivers and lakes. Since any reservoir is primarily a fresh water system, presence of a large array of zooplankton (diverse community structure) implies its good health and also presence of optimal dissolved oxygen levels supports sustenance of life. In this study, artificial neural network modelling approach has been utilized to predict the level of dissolved oxygen and zooplankton abundance in the Bakreswar reservoir and their variation in relation to the environmental factors. Use of neural network modelling is exceedingly capable of determining correlation among apparently non correlated environmental data and in the current study these are capable of accurately predicting the variations in the levels of dissolved oxygen as well as the abundance of zooplankton. From this study, it has been observed that chemical factors like productivity, nitrates, salinity, pH , phosphates, total dissolved solids, etc. are mostly responsible for control of dissolved oxygen and zooplankton variation at certain points of the study site (stations 1 and 3) whereas at other points (station 2) physical factors like solar radiation, humidity, etc. are more effective. These models are capable of finding the important environmental controllers of such variations and prove to be a powerful alternative to traditional approaches like multiple regression analysis. [ABSTRACT FROM AUTHOR]