Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data

Vakili, M.; Sabbagh-Yazdi, Saeed-Reza; Khosrojerdi, S.; Kalhor, Koosha

doi:10.1016/j.jclepro.2016.09.145

Cited by 90 publications

(33 citation statements)

References 39 publications

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“…ANNs have robust learning and generalization ability, after simulating the learning and decision-making process of human beings. Therefore, ANNs can describe the linear or non-linear relationships precisely even with limited input variables [27,28] and identify complex patterns in dataset without adequate understanding of the interaction among variables [24]. Besides this, because the learning mechanism in ANNs is non-parametric, the structure and distribution of data are not limitations [29].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, many commonly employed techniques for measurement focus on quantifying the diffusive flux of gases across the air-water interface, which is suitable for CO 2 because of its solubility [12]. There are some other key factors that affect model performance, such as the architecture selection and parameter settings [28]. However, it is difficult to reach any conclusion of which model architecture is absolutely suitable to a particular circumstance.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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“…Bou-Rabee ve arkadaşları, Kuveyt için günlük ortalama güneş ışınım değerlerini tahmin eden bir modeli yapay sinir ağlarını kullanarak geliştirmişlerdir [32]. Vakili ve arkadaşları, meteorolojik verilere bağlı yapay sinir ağları kullanılarak yapılan günlük ışınım değerlerinin tahmininde partiküller madde kirliliğinin etkisini inceleyerek tahmin modelinin verimliliğini arttırmışlardır [33]. Xue, günlük yayılan güneş ışınımını tahmininde geri yayılım sinir ağı modelinin etkinliğini ve genelleme kabiliyetini arttırmak için genetik algoritma (GA) ve parçacık sürüsü optimizasyonu (PSO) tekniklerini kullanmıştır [34].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Arslan

Bayhan

Yaman

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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ÖzBu çalışmada, Nisan 2017 -Mart 2018 tarihleri aralığında Mersin için ölçülen günlük toplam global güneş ışınım değerlerinin yapay sinir ağı kullanılarak modellenmesi yapılmıştır ve literatürde bulunan yaygın modellerin günlük toplam global güneş ışınım değerlerini tahmin etme performansları incelenmiştir. Günlük ortalama hava sıcaklığı, bağıl nem, rüzgar hızı, güneşlenme süresi ve bulut kapalılığı verileri, Devlet Meteoroloji İşleri Genel Müdürlüğü'nden temin edilmiş olup güneş ışınım değerleri ise piranometre ile ölçülmüştür. Sonuç olarak, incelenen modeller içerisinde en iyi tahmin performansını belirlilik katsayısı (R 2 ) 0,83 olan Model 37 (Zhang ve Huang) göstermiştir. AbstractIn this study, the daily total global solar radiation values measured for Mersin between April 2017 and March 2018 were modeled using artificial neural networks and the performance of estimating daily total global solar radiation values of the common models in the literature was investigated. Daily average air temperature, relative humidity, wind speed, sunshine duration, and cloud cover data are obtained from the Turkish State Meteorological Service and solar radiation values are measured with a pyranometer. As a result, Model 37 (Zhang and Huang) showed the best prediction performance among the models examined, with the coefficient of determination (R 2 ) being 0.83.

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“…This index ranges from 0 to 500, which is divided into different classifications, and each classification is related to different levels of human health. The country of Iran experiences considerable air pollution in different cities; this air pollution is usually measured and evaluated with the index, and the classification changes accordingly [31]. For example, when the index value is within the range of 0 to 50, the air quality is good, and when the index value is greater than 300, the air quality is very dangerous.…”

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confidence: 99%

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

et al. 2019

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The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation.

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Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data

Cited by 90 publications

References 39 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

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