Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Alizamir, Meysam; Shiri, Jalal; Fard, Ahmad Fakheri; Kim, Sungwon; Gorgij, Alireza Docheshmeh; Heddam, Salim; Singh, Vijay P.

doi:10.1016/j.engappai.2023.106199

Cited by 27 publications

(8 citation statements)

References 43 publications

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“…The mathematical formulation of the MARS model can be established in the form of basis function (BFs) with an ensemble of parameters determined during the training stage. The nonlinear function f (X) linking the input to the output variables can be expressed using MARS model as follows [31][32][33][34][35]:…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

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Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Alizamir

Kazemi

Kermani

et al. 2023

Water

Self Cite

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The likelihood of surface water and groundwater contamination is higher in regions close to landfills due to the possibility of leachate percolation, which is a potential source of pollution. Therefore, proposing a reliable framework for monitoring leachate and groundwater parameters is an essential task for the managers and authorities of water quality control. For this purpose, an efficient hybrid artificial intelligence model based on grey wolf metaheuristic optimization algorithm and extreme learning machine (ELM-GWO) is used for predicting landfill leachate quality (COD and BOD5) and groundwater quality (turbidity and EC) at the Saravan landfill, Rasht, Iran. In this study, leachate and groundwater samples were collected from the Saravan landfill and monitoring wells. Moreover, the concentration of different physico-chemical parameters and heavy metal concentration in leachate (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, Ca, Na, NO3, Cl, K, COD, and BOD5) and in groundwater (Cd, Cr, Cu, Fe, Ni, Pb, Mn, Zn, turbidity, EC, TDS, pH, Cl, Na, NO3, and K). The results obtained from ELM-GWO were compared with four different artificial intelligence models: multivariate adaptive regression splines (MARS), extreme learning machine (ELM), multilayer perceptron artificial neural network (MLPANN), and multilayer perceptron artificial neural network integrated with grey wolf metaheuristic optimization algorithm (MLPANN-GWO). The results of this study confirm that ELM-GWO considerably enhanced the predictive performance of the MLPANN-GWO, ELM, MLPANN, and MARS models in terms of the root-mean-square error, respectively, by 43.07%, 73.88%, 74.5%, and 88.55% for COD; 23.91%, 59.31%, 62.85%, and 77.71% for BOD5; 14.08%, 47.86%, 53.43%, and 57.04% for turbidity; and 38.57%, 59.64%, 67.94%, and 74.76% for EC. Therefore, ELM-GWO can be applied as a robust approach for investigating leachate and groundwater quality parameters in different landfill sites.

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Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

“…Consequently, the second step is reserved for the pruning of the model by deleting some of that irrelevant BF. Finally, in the third step, the model is fixed using only a sequence of sampler BF [32][33][34].…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Alizamir

Kazemi

Kermani

et al. 2023

Water

Self Cite

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“…The objectives of this study are twofold: first, to develop and evaluate AI-based power forecasting models for renewable energy microgrids, and second, to assess their environmental implications. The research aims to contribute to the existing body of knowledge by investigating the effectiveness of different AI techniques in accurately predicting renewable energy generation within a microgrid context [9].…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy MicroGrid Power Forecasting: AI Techniques with Environmental Perspective

Islam,

Othman

2024

Preprint

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This research focuses on the accurate prediction of renewable energy generation in microgrid systems using artificial intelligence (AI) techniques. The study compares and evaluates different AI models, including Artificial Neural Networks (ANN), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), for forecasting solar and wind power output. The models are trained and tested using real-world data, and their performance is assessed using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Sum of Squared Errors (SSE). The results demonstrate that the ANN model achieves the lowest RMSE and MAPE values for wind power prediction, while the Fuzzy Logic model performs well in predicting solar power generation. These findings indicate the effectiveness of AI techniques in accurately forecasting renewable energy output in microgrid systems. The proposed approach has implications for optimizing the utilization and integration of renewable energy sources, leading to more efficient and sustainable microgrid operations. Future research directions may involve exploring advanced deep-learning models and incorporating additional environmental factors to further enhance the accuracy and reliability of renewable energy power forecasting in microgrids.

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“…For the past twenty years, artificial intelligence techniques have been utilized successfully in various engineering applications, particularly for water resource problems and hydrological studies and these methods have demonstrated remarkable efficacy and precision [38,39]. Delbari et al (2019) [40] examined the effectiveness of a model based on support vector regression (SVR) in approximating the daily soil temperature at various depths (10, 30, and 100cm) under various weather patterns.…”

Section: Introductionmentioning

confidence: 99%

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Alizamir,

Ahmed,

Kim

et al. 2023

PLoS ONE

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Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.

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Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Cited by 27 publications

References 43 publications

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Renewable Energy MicroGrid Power Forecasting: AI Techniques with Environmental Perspective

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

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