Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

Outlier detection is an essential task in data mining applications which include, military surveillance, tax fraud detection, telecommunication, etc. In recent years, outlier detection received significant attention compared to other problem of discoveries. The focus on this has resulted in the growth of several outlier detection algorithms, mostly concerning the strategy based on distance or density. However, each strategy has intrinsic weaknesses. The distance-based techniques have the problem of local density, while the density-based method is recognized as having an issue of a low-density pattern. Also, most of the existing outlier detection algorithms have a parameter selection problem, which leads to poor detection results. In this article, we present an unsupervised density-based outlier detection algorithm to deal with these shortcomings. The proposed algorithm uses a Natural Neighbour (NaN) concept, to obtain a parameter called Natural Value (NV) adaptively, and a Weighted Kernel Density Estimation (WKDE) method to estimate the density at the location of an object. Besides, our proposed algorithm employed two different categories of nearest neighbours, k Nearest Neighbours (kNN), and Reverse Nearest Neighbours (RNN), which make our system flexible in modelling different data patterns. A Gaussian kernel function is adopted to achieve smoothness in the measure. Further, we use an adaptive kernel width concept to enhance the discrimination power between normal and outlier samples. The formal analysis and extensive experiments carried out on both artificial and real datasets demonstrate that this technique can achieve better outlier detection performance.