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In this study, the support vector machine (SVM) technique was used to estimate the permeability of the soil. The performance of SVM model was equated with another type of artificial intelligence techniques such as Gaussian process (GP), random forest (RF) and multi-linear regression (MLR)-based models. While a GP, RF, and MLR model gives a good estimation performance, the SVM model outperforms them. Two kernel functions, Pearson VII and radial basis kernel function, were used in SVM and GP regression models. For this study, a data set contaning 95 observations collected from laboratory experiments. Out of 95, a total of 66 data sets were selected for preparing different algorithms whereas rest 29 data sets were selected to test the models. Input variables include percentage of sand (S), percentage of fly ash (Fa), specific gravity (G), time (T) and head (H), whereas coefficient of permeability (k) is considered as output. A comparison was also done with the various study, which shows poor relationship between the values of permeability. Sensitivity analysis concludes that the parameter, time and water head are the most effective parameters for the estimation of permeability of soil. [ABSTRACT FROM AUTHOR]

Particulate matter has a detrimental consequence on the health of living organisms throughout the world and predicting their concentration is very imperative to assess their impact on human health. Faridabad is the most populated and largest city of Haryana, India, and the current study was designed to foresee the PM2.5 content by different modeling techniques: (1) support vector machine (SVM), (ii) random forest (RF), (iii) artificial neural network (ANN), (iv) M5P model, and (v) Gaussian process regression (GP). Collected data (659 observations) from May 2015 to May 2018 were used to develop the models. Parameters such as temperature (T), ground-level ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitric oxide (NO), NOx, wind speed (WS), wind direction (WD), relative humidity (RH), bar pressure (BP), and solar radiation (SR) are used as input parameters for prediction of PM2.5. The results of all the models suggested that RF model with testing correlation coefficient (CC) 0.8312, mean absolute error (MAE) 30.7757, R2 (correlation of determination) 0.6909, and root mean square error (RMSE) 44.6947 is the best estimator for appraisal of PM2.5 followed by SVM, GP, M5P, and ANN models. The sensitivity analysis results indicated that wind speed is the utmost influencing parameter for the estimation of PM2.5. Abilities of different models were compared and RF was established as the best technique based on assessment criteria. We recommend more studies employing RF and other techniques as hybrids that lead to better models. [ABSTRACT FROM AUTHOR]