scholarly journals Forecasting the CO2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6336
Author(s):  
Pradyot Ranjan Jena ◽  
Shunsuke Managi ◽  
Babita Majhi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Co2 Emissions ◽  
Prediction Accuracy ◽  
Climate Mitigation ◽  
Growth Path ◽  
Optimal Weights ◽  
Mitigation And Adaptation ◽  
Artificial Neural ◽  
Emission Growth

Better accuracy in short-term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations.

scholarly journals Application of Wavelet Neural Network in Building Settlement Prediction

2020 ◽  
Vol 198 ◽  
pp. 03014
Author(s):  
Ruijie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Building Design ◽  
Wavelet Neural Network ◽  
Deformation Monitoring ◽  
Polynomial Method ◽  
Observation Data ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

2021 ◽  
Author(s):  
Asma Elyounsi ◽  
Hatem Tlijani ◽  
Mohamed Salim Bouhlel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Problem Size ◽  
Functional Link ◽  
Optimal Weights ◽  
Feed Forward Network ◽  
Higher Order Neural Networks ◽  
Artificial Neural

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.

scholarly journals An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 113 ◽  
Author(s):  
Joao Ferreira ◽  
Gustavo Callou ◽  
Albert Josua ◽  
Dietmar Tutsch ◽  
Paulo Maciel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Data Center ◽  
Data Centers ◽  
New Technologies ◽  
Operational Costs ◽  
The Us ◽  
Artificial Neural

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

scholarly journals Grade Diagnosis of Human Glioma Based on Fingerprint and Artificial Neural Network

2021 ◽  
Author(s):  
Wenyu Peng ◽  
Shuo Chen ◽  
Dongsheng Kong ◽  
Xiaojie Zhou ◽  
Xiaoyun Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Pathological Diagnosis ◽  
World Health ◽  
Low Grade ◽  
Human Glioma ◽  
Who Grade ◽  
Specificity And Sensitivity ◽  
Artificial Neural

Abstract BackgroundThe World Health Organization (WHO) grade diagnosis of cancer is essential for surgical outcomes and patient treatment. Traditional pathological grading diagnosis depends on dyes or other histological approaches, which are time-consuming (usually 1-2 days), resource-wasting, and labor-intensive. Fourier transform infrared (FTIR) spectroscopy is a rapid and nondestructive technique that has been widely used for detecting the molecular component changes, which relies on the resonant frequencies absorbance of the molecular bonds.MethodsTo overcome the disadvantages of traditional pathological diagnosis, this paper proposed a novel diagnostic method based on FTIR and artificial neural network (ANN). Firstly, the spectra of high- and low-grade human glioma that without dye were collected by FTIR spectrometer, then the raw data preprocessed with baseline correction and amide I (1649 cm-1) normalization before input into the input-layer of the ANN, after the nonlinear conversion of the neurons in the hidden-layers, the categories were presented in the output-layer. Corresponding to the decrease of the loss function, the weights of the net updated continuously, and finally, the optimized model has the power of prediction for new samples. ResultsAfter training on 6225 spectra sourced from 77 glioma patients, the ANN model reached the prediction accuracy, specificity and sensitivity evaluation metrics above 99%, which was much superior to the common classification method of principal component analysis-linear discriminate analysis (PCA-LDA) (the prediction accuracy, specificity and sensitivity are only 87%, 89% and 86%, respectively). Moreover, rather than the lipid range of 2800-3000 cm-1, the ANN learned the fingerprint characteristics of the infrared spectrum to classify the major histopathologic classes of human glioma. Especially, the diagnosis process of the novel method only requires several minutes. Compared to the traditional pathological diagnosis, the efficiency raises almost 500 times.ConclusionsThe infrared range of fingerprint is the major indicator for cancer progression, and the ANN-based diagnosis method can be streamlined, and create a complementary pathway that is independent of the traditional pathology laboratory.

scholarly journals Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach

2016 ◽  
Vol 1 (2) ◽  
pp. 77-84
Author(s):  
Ogbeide K. O. ◽  
Eko Mwenrenren E. J.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Artificial Neural Network Model ◽  
Path Loss ◽  
Signal Propagation ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Edo State ◽  
Artificial Neural

The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.

Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

2019 ◽  
Vol 120 (2) ◽  
pp. 312-328 ◽  
Author(s):  
Wei Qin ◽  
Huichun Lv ◽  
Chengliang Liu ◽  
Datta Nirmalya ◽  
Peyman Jahanshahi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Filter ◽  
Prediction Accuracy ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Content Type ◽  
Public Data ◽  
Useful Life ◽  
Artificial Neural

Purpose With the promotion of lithium-ion battery, it is more and more important to ensure the safety usage of the battery. The purpose of this paper is to analyze the battery operation data and estimate the remaining life of the battery, and provide effective information to the user to avoid the risk of battery accidents. Design/methodology/approach The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used as the observation equation. After the importance resampling process, the battery degradation curve is obtained after getting the posterior parameter, and then the system could estimate remaining useful life (RUL). Findings Experiments were carried out by using the public data set. The results show that the Bayesian-based posterior estimation model has a good predictive effect and fits the degradation curve of the battery well, and the prediction accuracy will increase gradually as the cycle increases. Originality/value This paper combines the advantages of the data-driven method and PF algorithm. The proposed method has good prediction accuracy and has an uncertain expression on the RUL of the battery. Besides, the method proposed is relatively easy to implement in the battery management system, which has high practical value and can effectively avoid battery using risk for driver safety.

Mapping soil nail loads using Federal Highway Administration (FHWA) simplified models and artificial neural network technique

2020 ◽  
Vol 57 (10) ◽  
pp. 1453-1471 ◽  
Author(s):  
Peiyuan Lin ◽  
Pengpeng Ni ◽  
Chengchao Guo ◽  
Guoxiong Mei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Limit States ◽  
Operational Conditions ◽  
Soil Nail ◽  
Federal Highway ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study compiles a broad database containing 312 measured maximum soil nail loads under operational conditions. The database is used to re-assess the prediction accuracies of the default Federal Highway Administration (FHWA) nail load model and its modified version previously reported in the literature. Predictions using the default and modified FHWA models are found to be highly dispersive. Moreover, the prediction accuracy is statistically dependent on the magnitudes of the predicted nail load and several model input parameters. The modified FHWA model is then recalibrated by introducing extra empirical terms to account for the influences of wall geometry, nail design configuration, and soil shear strength parameters on the evolvement of nail loads. The recalibrated FHWA model is demonstrated to have much better prediction accuracy compared to the default and modified models. Next, an artificial neural network (ANN) model is developed for mapping soil nail loads, which is shown to be the most advantageous one as it is accurate on average and the dispersion in prediction is low. The abovementioned dependency issue is also not present in the ANN model. The practical value of the ANN model is highlighted by applying it to reliability-based designs of soil nails against internal limit states.

scholarly journals Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

2012 ◽  
Vol 14 (4) ◽  
pp. 974-991 ◽  
Author(s):  
Shouke Wei ◽  
Depeng Zuo ◽  
Jinxi Song
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
River Discharge ◽  
Prediction Accuracy ◽  
Hybrid Approach ◽  
Series Data ◽  
Discharge Time ◽  
Artificial Neural

This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.

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