Physicochemical Habitat Traits Preferred by Small Indigenous Fish (Chanda Nama ) in Indian River Discerning through Machine Learning

Abstract

Physicochemical traits of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with aquatic factors in river. Machine learning (ML) modeling is a useful tool to established relationship between complex systems. This study identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River, of peninsular India, using machine learning modeling. Data were observed on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites on the river during year 2001-02. Machine learning models such as random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN) used for the classification of Chanda nama distribution in the river. The ML model efficiency was evaluated using classification accuracy (CCI), Cohen’s kappa coefficient (k), sensitivity, specificity and receiver-operating-characteristics (ROC). Results showed that random forest is the best model with 82% accuracy, CCI (0.82), k (0.55), sensitivity (0.57), specificity (0.76) and ROC (0.72) for Chanda nama distribution (presence/absence) in the Krishna River. Random Forest model identified three preferred physicochemical habitat traits like altitude, temperature and depth for Chanda nama distribution in the Krishna River, India. This study will be helpful for researcher and policy maker to understand the important habitat physicochemical traits for sustainable management of small indigenous fish (Chanda nama ) in the river system.