A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

Lotfinejad, Mohammad Mehdi; Hafezi, Reza; Khanali, Majid; Hosseini, Seyed Sina; Mehrpooya, Mehdi; Shamshirband, Shahaboddin

doi:10.3390/en11051188

Cited by 42 publications

(12 citation statements)

References 52 publications

(66 reference statements)

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“…The proposed hybrid metaheuristic LSSVM-BA algorithm is used for the prediction of DO concentration. The BA has been widely utilized for parameter optimization of the model used in forecasting climatological variables [62,63] and reservoir operation [64,65]. The studies reported BA as an efficient optimization technique.…”

Section: Introductionmentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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Abstract:The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

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Section: Introductionmentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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“…Lotfinejad et al . demonstrated bat neural network, which uses a novel multilayer framework for a particular combination of the bat algorithm (BA) and a three‐layer artificial neural network (ANN) to estimate daily solar radiation over Iran. The parameters of ANN can be optimized by BA.…”

Section: Introductionmentioning

confidence: 99%

The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network

Luo

Zhu

Jiang

et al. 2020

IEEJ Transactions Elec Engng

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The 10.7‐cm solar radio flux (F10.7) is among the most widely used indices of solar activity, whose prediction plays a vital role in the field of meteorology and aerospace. In this paper, a novel approach for a specific amalgamation of back propagation neural network (BPNN) and empirical mode decomposition (EMD) is proposed, which is called EMD‐BP, aimed at forecasting the daily F10.7 values 1–27 days ahead. The daily F10.7 values, which are highly nonlinear and unstable time series, are transformed into a series of subsignals with different frequency components by EMD and trained by the BPNN. For estimating the performance of the description EMD‐BP, the support vector regression (SVR), Long Short‐Term Memory neural network and BPNN as the reference models are analyzed in different conditions. The results indicate that the proposed model improves the F10.7 forecasting within 27 days, compared with the common machine learning algorithms and transport model. Furthermore, a tremendous decline of forecast error is determined in high‐level solar activity, in contrast with SVR and BPNN. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Crop cultivation is difficult to model as it is a complex and nonlinear process. Soft-computing approaches have revealed efficient solutions for nonlinear natural process such as flood, drought, wind, and rainfall (Asadi, Shahrabi, Abbaszadeh, & Tabanmehr, 2013;Fortin, Anctil, Parent, & Bolinder, 2010;Gago, Martínez-Núñez, Landín, & Gallego, 2010;Lotfinejad et al, 2018;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Mosavi, Bathla, & Varkonyi-Koczy, 2017;Nazari & Shamshirband, 2018;Sehgal, Sahay, & Chatterjee, 2014;Tiwari & Chatterjee, 2011). Some of the previous works have used soft-computing method for solving problem related to agriculture industry.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Ghazvinei

Darvishi

Mosavi

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production datadriven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R 2 ) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results. ARTICLE HISTORY

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A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

Cited by 42 publications

References 52 publications

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

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