Ability of machine learning models to identify preferred habitat traits of a small indigenous fish (Chanda nama) in a large river of peninsular India.

Physical and chemical parameters of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with different aquatic factors in river. Machine learning modeling is a useful tool to identify relationships between components of a complex environmental system. We identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River located in peninsular India, using machine learning modeling. Using data on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites of the river collected during the year 2001-2002, machine learning models such as random forest, artificial neural network, support vector machine, and k-nearest neighbors were used for classification of Chanda nama distribution in the river. The machine learning model efficiency was evaluated using classification accuracy, Cohen's kappa coefficient, sensitivity, specificity, and receiver-operating-characteristics. Results showed that random forest is the best model with higher classification accuracy (82%), Cohen's kappa coefficient (0.55), sensitivity (0.57), specificity (0.76), and receiver-operating-characteristics (0.72) for prediction of the occurrence of Chanda nama in the Krishna River. Random forest model identified three preferred physicochemical habitat traits such as altitude, temperature, and depth for Chanda nama distribution in Krishna River. Our results will be helpful for researcher and policy maker to understand important physical and chemical variables for sustainable management of a small indigenous fish (Chanda nama) in a large tropical river.