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The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone. [ABSTRACT FROM AUTHOR]

There are many debates on the preparation methods and the role of ultrasonication on the stability, thermophysical properties, and heat transfer performance of nanofluids. The present study, which is the continuation of the authors previous study, the effects of ultrasonication on the thermal and fluid dynamic performance of MWCNT-water nanofluid, over a different range of temperatures and solid concentrations, based on the thermophysical properties of the nanofluid, has been investigated. The effects of ultrasonication time on the stability and thermophysical properties of the nanofluid were studied over 30 days of the samples preparation. The thermophysical properties of the nanofluid have been experimentally measured at the optimum ultrasonication time. Using the experimental data, and employing different figures-of-merit, the effects that the addition of MWCNTs had on the heat transfer effectiveness and pumping power have been studied. It was confirmed that the nanofluid is a good heat transfer fluid, with a negligible penalty in pumping power. The thermal and fluid dynamic performance of the nanofluid in a microchannel heat sink has also been studied, by comparing the enhancement ratio of the convective heat transfer coefficient and the increase in pumping power. [ABSTRACT FROM AUTHOR]

The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings. [ABSTRACT FROM AUTHOR]

In this paper, the Multi-Layer Perceptron (MLP) neural network is optimized with two metaheuristic algorithms, namely Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) for estimating the heating load (HL) and cooling load (CL) of the energy efficient buildings with the residential use. To achieve this, a dataset composed of eight independent factors namely, relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, along with the two dependent variables of HL and CL is provided. Out of 768 samples, 80:20 ratio is considered to select the training and testing datasets randomly. Through a trial and error process, the optimal parameters of the MLP, ABC-MLP and PSO-MLP networks are determined. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the used models. The results reveal that applying the ABC and PSO algorithms, helps the MLP to perform more efficiently. In this sense, the increase of R2 from 0.8933 to 0.9349 and 0.9370 for the HL, and from 0.8872 to 0.8969 and 0.8997 for the CL prediction show that the outputs of the ensemble models (i.e., ABC-MLP and PSO-MLP) are more correlated with the actual data. Also, the MAE decreases as 22.32% and 24.28% for the HL, and 10.36% and 12.00% for the CL respectively by applying the ABC and PSO. Besides, the RSME decreases as 22.48% and 23.86% for the HL, and 6.06% and 7.56% for the CL modeling respectively with using the ABC and PSO. It is also deduced that the PSO outperforms the ABC in the performance enhancement of the MLP. • Neural network is optimized to predict heating and cooling loads of a residential building. • Through a trial and error process, optimal parameters of different models are determined. • Applying metaheuristic algorithms helps neural network to perform more efficiently. • Particle swarm optimization outperforms artificial bee colony in improving neural network. [ABSTRACT FROM AUTHOR]