Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quality over traditional methods. In this study, the performance of nine different ML algorithms is evaluated to predict nitrate and phosphorus concentration for five different watersheds with different land-use practices. The land-use distribution affects the model performance for all methods. In urban watersheds, the regular and predictable nature of nitrate concentration from wastewater treatment plants results in more accurate estimates. For the nitrate prediction, ANN outperforms other ML models for the urban and agricultural watersheds, while RT-BO performs well for the forested Grand watershed. For the total phosphorus prediction, ensemble-BO and M-SVM outperform other ML models for the agricultural and forested watershed, while the ANN performs better than other ML models for the urban Cuyahoga watershed. In predicting phosphorus concentration, the model predictability is better for agricultural and forested watersheds. Regarding consistency, Bayesian optimized RT, ensemble, and GPR consistently yielded good performance for all watersheds. The methodology and results outlined in this study will assist policymakers in accurately predicting nitrate and phosphorus concentration which will be instrumental in drafting a proper plan to deal with the problem of water pollution.

This paper presents archimedes optimization algorithm(AOA) and dispersive flies optimization(DFO) to optimally tune gain parameters of PID control scheme in order to regulate DC motor’s speed. These suggested techniques tune the controller by the minimization of the fitness function represented by the integral of time multiplied by absolute error (ITAE). The modelling and simulation are carried out in MATLAB/Simulink. The transient response of unit step input obtained from AOA-PID-ITAE andDFO-PID-ITAE controllers were compared to those obtained from Ziegler-Nichols (ZN) method and particle swarm optimization(PSO). The results indicate that AOA-PID-ITAE and DFO-PID-ITAE are more efficient than ZN method and PSO in reducing rise time and settling time. Likewise, DFOconverge faster to the optimal solution with lower overshoot than AOA and PSO.

Global solar radiation (GSR) is a critical variable for designing photovoltaic cells, solar furnaces, solar collectors, and other passive solar applications. In Nepal, the high initial cost and subsequent maintenance cost required for the instrument to measure GSR have restricted its applicability all over the country. The current study compares six different temperature-based empirical models, artificial neural network (ANN), and other five different machine learning (ML) models for estimating daily GSR utilizing readily available meteorological data at Biratnagar Airport. Amongst the temperature-based models, the model developed by Fan et al. performs better than the rest with anR2of 0.7498 and RMSE of2.0162MJm−2d−1. Feed-forward multilayer perceptron (MLP) is utilized to model daily GSR utilizing extraterrestrial solar radiation, sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity as inputs. ANN3 performs better than other ANN models with anR2of 0.8446 and RMSE of1.4595MJm−2d−1. Likewise, stepwise linear regression performs better than other ML models with anR2of 0.8870 and RMSE of1.5143MJm−2d−1. Thus, the model developed by Fan et al. is recommended to estimate daily GSR in the region where only ambient temperature data are available. Similarly, a more robust ANN3 and stepwise linear regression models are recommended to estimate daily GSR in the region where data about sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity are available.

Project cost prediction is one of the key elements in the civil engineering activities development. Project cost is a highly sensitive component to diverse parameters and hence it is associated with complex trends that make it difficult to be predicted and fully understood. Due to the massive advancement of soft computing (SC) and Internet of things (IoT), the main research objective of the current study was initiative. Several machine learning (ML) models including extreme learning machine (ELM), multivariate adaptive regression spline (MARS), and partial least square regression (PLS) were adopted to predict field canal cost. Several essential predictors were used to develop the prediction network “the learning process” including the total length of the PVC pipeline, served area, geographical zone, construction year, and cost and duration of field canal improvement projects (FCIP) construction. Data were collected from the open source published literature. The modeling results evidenced the potential of the applied SC models in predicting the FCIP cost. In numerical magnitude evaluation, MARS model indicated the least value for the root mean square error (RMSE = 27422.7), mean absolute error (MAE = 19761.8), and mean absolute percentage error (MAPE = 0.05454) with Nash–Sutcliffe efficiency (NSE = 0.94), agreement index (MD = 0.89), and coefficient of determination (R² = 0.94), with best precision of prediction using all predictors, except geographical zone parameter in which less influence on the cost construction is presented. In general, the research outcome gave an informative primary cost initiative for cost civil engineering project.