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Imbalance in the pH of water reduces this precious resource as an extremely dangerous liquid for human health and plants’ growth. Change in the pH levels of the drinkable water has majorly raised concern towards diverse health issues like heart problems, infant mortality rates, pigmentation of skin, and cholera outbreaks. Therefore, it is necessary to keep a check on essential water quality components that include acidic/basic nature of water. As per the US Environmental Protection Agency (USEPA), the drinkable water should have a pH level ranging from 6.5 to 8.5. Two sample situates have been identified wherever highly reported pollutants levels were found and have been analyzed through artificial intelligence (AI) techniques. It can be observed that wavelet denoised signals fed into the least squares support vector regression (LSSVR) and M5 prime regression tree (M5pRT) predicted more accurately on the basis of the performance errors that are as follows: (a) root mean squared error (RMSE); (b) mean squared error (MSE); (c) mean absolute error (MAE). On the basis of these errors, the coefficient of determination/goodness of fit (R2) simulated for the prototypes is developed in this study. RMSE outcomes diminish on the whole on applying the training and forecasting data-division via WLSSVR and WM5pRT as compared with fitting the normalized data through LSSVR and M5pRT. These performance measures are essential to analyze the concentration levels of pH in the river streams at the identified sites of study. Thus, the observed pattern from this study may help for future estimation of the quality of water at their sources so that it prohibits the further increase in either acidic or basic salts which prove to be lethal for the environment. Thus, these predictors would be helpful towards formulation of strategies for protection of ecosystem and human health.

Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50-60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R2 varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.