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The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg +2 ), and sulfate (SO 4 -2 ) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg +2 , and SO 4 -2 . The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg +2 and SO 4 -2 . The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg +2 and SO4 -2 , respectively., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This correction notice provides updated affiliation details for one of the authors, Hai Tao, in an article titled "High-resolution remote sensing data-based urban heat island study in Chongqing and Changde City, China." The original article incorrectly listed Hai Tao's affiliation as the 'GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia,' but the correct affiliations are the School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang, China; the School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, China; and the Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE. The correction has been made to the original article. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

The scouring process near spur dikes could jeopardize the stability of riverbanks. Thus, accurate estimation of the maximum scour depth near spur dikes is crucial in river engineering. However, due to the complexity of the scour phenomenon around these structures, it has been a challenge to accurately estimate the maximum scour depth. Few efforts have been made to develop a machine learning (ML) approach for such a purpose. In this study, two novel multilayer stacked generalization frameworks are developed to model the scour depth near a spur dike in a uniform sediment condition. Stacked tree-based frameworks consist of three standalone ML approaches, including multivariate adaptive regression spline, multigene genetic programming, and kernel extreme learning machine as the first layer and the boosting regression tree (BRT) and bagging regression tree (BGT) as meta learners. A total of 186 data points were collected from previous experimental studies, and 32 flume experiments were further conducted under clear water conditions at the Indian Institute of Technology (IIT) Roorkee Laboratory in this study. The performances of the models were assessed using various statistical metrics [e.g., correlation coefficient (R), root-mean-square error (RMSE), and mean absolute percentage error (MAPE)], graphical criteria, and some existing empirical equations. The modeling results demonstrated that the stacked BRT and BGT frameworks were superior to all the standalone ML approaches (R=0.9786 , RMSE=0.1654 , and MAPE=11.68 for BRT; and R=0.9742 , RMSE=0.1831 , and MAPE =11.29 for BGT). This finding was also confirmed by a comparison between the empirical correlations and the artificial intelligence models. In addition, the sensitivity analysis proved that the mean sediment size ratio (L/d50) was the most influential variable in estimating the scour depth near spur dikes. [ABSTRACT FROM AUTHOR]

The scour phenomena around vertical piles in oceans and under waves may influence the structure stability. Therefore, accurately predicting the scour depth is an important task in the design of piles. Empirical approaches often do not provide the required accuracy compared with data mining methods for modeling such complex processes. The main objective of this study is to develop three data-driven methods, locally weighted linear regression (LWLR), support vector machine (SVR), and multivariate linear regression (MLR) to predict the scour depth around vertical piles due to waves in a sand bed. It is the first effort to develop the LWLR to predict scour depth around vertical piles. The models simulate the scour depth mainly based on Shields parameter, pile Reynolds number, grain Reynolds number, Keulegan–Carpenter number, and sediment number. 111 laboratory datasets, derived from several experimental studies, were used for the modeling. The results indicated that the LWLR provided highly accurate predictions of the scour depths around piles (R = 0.939 and RMSE = 0.075). Overall, this study demonstrated that the LWLR can be used as a valuable tool to predict the wave-induced scour around piles. [ABSTRACT FROM AUTHOR]

• A novel weighted mean of vectors (INFO) is proposed for global optimization. • The performance of INFO is verified by comparison against other competitive algorithms. • INFO has faster convergence speed and accuracy compared with others. • INFO was validated on 48 functions and four remarkable engineering test cases. • Excellent results have been obtained in engineering experiments. This study presents the analysis and principle of an innovative optimizer named weIghted meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean method, whereby the weighted mean idea is employed for a solid structure and updating the vectors' position using three core procedures: updating rule , vector combining , and a local search. The updating rule stage is based on a mean-based law and convergence acceleration to generate new vectors. The vector combining stage creates a combination of obtained vectors with the updating rule to achieve a promising solution. The updating rule and vector combining steps were improved in INFO to increase the exploration and exploitation capacities. Moreover, the local search stage helps this algorithm escape low-accuracy solutions and improve exploitation and convergence. The performance of INFO was evaluated in 48 mathematical test functions, and five constrained engineering test cases including optimal design of 10-reservoir system and 4-reservoir system. According to the literature, the results demonstrate that INFO outperforms other basic and advanced methods in terms of exploration and exploitation. In the case of engineering problems, the results indicate that the INFO can converge to 0.99% of the global optimum solution. Hence, the INFO algorithm is a promising tool for optimal designs in optimization problems, which stems from the considerable efficiency of this algorithm for optimizing constrained cases. The source codes of INFO algorithm are publicly available at https://imanahmadianfar.com. and https://aliasgharheidari.com/INFO.html. [ABSTRACT FROM AUTHOR]

The main aim of the present study is to investigate the capabilities of four robust machine learning method - the Kernel Extreme Learning Machine (KELM), Adaptive Regression Spline (MARS), M5 Model Tree (M5Tree), and Gene Expression Programming (GEP) model in predicting specific heat capacity (SHC) of metal oxide-based nanofluids implemented in solar energy application. Sets of 1180 data of different metal oxide-based nanofluids containing Al 2 O 3 , ZnO, TiO 2 , SiO 2 , MgO, and CuO dispersed in various base fluids were collected from reliable literature to provide the predictive model of SHC of nanofluids. The volume fraction, temperature, SHC of the base fluid, and mean diameter of nanoparticles were used as an input variable to predict nanofluids' SHC as the output variable. The artificial intelligence (AI) models were validated using several statistical performance criteria, graphical devices, and conventional models. The results obtained from all datasets demonstrated that the KELM model significantly outperformed the MARS, M5Tree, and GEP model in predicting the SHC of nanofluid. Moreover, the sensitivity analysis showed that the mean diameter of the nanoparticle and SHC of the base fluid have the most considerable impact on estimating the SHC of metal oxide-based nanofluids. [ABSTRACT FROM AUTHOR]

Oil-based hybrid nanofluids play an important role in heat transfer in cooling systems and lubrication. Therefore, various experimental investigations are conducted to estimate their viscosity. However, such measurements can be carried out on limited types of oil-based hybrid nanofluids and often are time consuming and expensive. The main objective of this paper is to develop a rigorous data-driven method based on an advanced genetic programming (GP) called multigene genetic programming (MGGP) to predict the viscosity of Newtonian oil-based hybrid nanofluids which has not previously been used in this area. A comparative analysis was performed using the gene expression programming (GEP), multi-variate linear regression (MLR) methods and various correlations. 679 experimental data points with different nanoparticles and oil-based fluids were collected from literature to develop the Artificial Intelligent (AI) models. The new approach showed superior performance in estimating of the relative viscosity of oil-based hybrid nanofluids in comparison with all correlations methods. Furthermore, the MGGP results for the test dataset (R=0.991, RMSE=0.05, PI=0.643) were more accurate than those obtained from the GEP (R=0.975, RMSE=0.083, PI=0.696) and MLR (R=0.912, RMSE =0.153, PI=1), respectively. The sensitivity analysis was also performed demonstrating that the volume fraction (P I s =0.849, D V 1 =10.079%), temperature (P I s =0.463, D V 2 =9.966%) and nanoparticles size (P I s =0.420, D V 3 =6.092%) are the most significant factors in assessing relative viscosity, respectively. [ABSTRACT FROM AUTHOR]

Accurate prediction of thermal conductivity of hybrid nanofluids is very important for industries such as microelectronics and cooling applications that heavily rely on the heat transfer. Many experimental investigations are conducted aiming at developing correlations to predict the relative thermal conductivity of hybrid nanofluids. However, the proposed correlations are limited to specific types of hybrid nanofluids. In this research, for the first time three soft computing techniques namely, Genetic programming (GP), Model tree (MT) and Multi linear regression (MLR) models, were developed and used to accurately predict the thermal conductivity of various ethylene glycol (EG)-based hybrid nanofluids. A total of 275 datasets from literature were collected and divided into the testing and training groups. The results obtained from the proposed approaches were compared with a number of performance metrics and empirical correlations. The performance criteria indicated that the GP model for the test dataset (R = 0.950, RMSE = 0.0225) had the best prediction performance for the relative thermal conductivity of hybrid nanofluids in comparison to MT (R = 0.928, RMSE =0.0301) and MLR (R = 0.787, RMSE =0.050), respectively. Sensitivity analysis showed that the nanoparticle volume fraction (R = 0.445, SI = 0.0667) was the most influential factor among all model input parameters. [ABSTRACT FROM AUTHOR]

River flow (Q flow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Q flow modeling. Q flow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000–2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input parameters used for model development, Q flow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Q flow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication. [ABSTRACT FROM AUTHOR]