Reliable prediction of water quality changes is a prerequisite for early water pollution control and is vital in environmental monitoring, ecosystem sustainability, and human health. This study uses Artificial Neural Network (ANN) technique to develop the best model fits to predict water quality parameters by employing multilayer perceptron (MLP) neural network and the radial basis function (RBF) neural network, using data collected from three district municipalities. Two input combination models, MLP-4-5-4 and MLP-4-9-4, were trained, verified, and tested for their predictive performance ability, and their physicochemical prediction accuracy was compared by using each model’s observed data with the predicted data. The MLP-4-5-4 model showed a better understanding of the data sets and water quality predictive ability giving an MSE of 39.06589 and a correlation coefficient (R2) of the observed and the predicted water quality of 0.989383 compared to the MLP-4-9-4 model (R2 = 0.993532, MSE = 39.03087). These results apply to natural water resources management in South Africa and similar catchment systems. The MLP-4-5-4 system can be scaled up for future water quality prediction of the Waste Water Treatment Plants (WWTPs), groundwater, and surface water while raising awareness among the public and industry on future water quality.

Trypanosomes and Leishmania are parasitic protozoans that affect millions of people globally. Herein we report the synthesis of 2‐aroyl quinazolinones and their antiprotozoal efficacy against Trypanosoma brucei, Trypanosoma brucei rhodesiense, Trypanosoma cruzi, and Leishmania infantum. These compounds were counter‐screened against a human cell line for cytotoxicity. Thirteen of the twenty target compounds in this study inhibited the growth of these parasites, with compounds KJ1, and KJ10 exhibiting IC50 values of 4.7 μM (T. b. brucei) and 1.1 μM (T. b. rhodesiense), respectively.