Estimating Monthly Surface Air Temperature Using MODIS LST Data and an Artificial Neural Network in the Loess Plateau, China

Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.

A Measurement Study of the Ethereum Underlying P2P Network

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying nodes.Ethereum was applied because it pioneered distributed applications,smart contracts,and Web3.Moreover,its application layer language“Solidity”is widely used in smart contracts across different public and private blockchains.To this end,we wrote a new Ethereum client based on Geth to collect Ethereum node information.Moreover,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine project.The collected data has been compared with two other services that harvest the number of Ethereumnodes.Ourmethod has collectedmore than 30% more than the other services.The data trained a neural network model regarding time series to predict the number of online nodes in the future.Our findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change frequently.It poses a question of the stability of the network.Furthermore,historical data shows that the top ten countries with Ethereum clients have not changed since 2016.The popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node security.The results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other regions.It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology.Finally,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.

Application of Regularized Logistic Regression and Artificial Neural Network Model for Ozone Classification across El Paso County, Texas, United States

This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.

Application of Artificial Neural Networks to Rainfall Forecasting in Queensland,Australia

In this study, the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland, Australia, was assessed by inputting recognized climate indices, monthly historical rainfall data, and atmospheric temperatures into a prototype stand-alone, dynamic, recurrent, time-delay, artificial neural network. Outputs, as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009, were compared with observed rainfall data using time-series plots, root mean squared error （RMSE）, and Pearson correlation coefficients. A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology＇s Predictive Ocean Atmosphere Model for Australia （POAMA）-I.5 general circulation model （GCM） indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared. The application of artificial neural networks to rainfall forecasting was reviewed. The prototype design is considered preliminary, with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.

Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks

Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.

Wind Power Forecasting using an Artificial Neural Network for ASPCS

In order to use effectively renewable energy sources, we propose a new system, called Advanced Superconducting Power Conditioning System (ASPCS) that is composed of Superconducting Magnetic Energy Storage (SMES), Fuel Cell-Electrolyzer (FC-EL), hydrogen storage and DC/DC and DC/AC converters in connection with a liquid hydrogen station for fuel cell vehicles. The ASPCS compensates the fluctuating electric power of renewable energy sources such as wind and photovoltaic power generations by means of the SMES having characteristics of quick response and large Input-Output power, and hydrogen energy with FC-EL having characteristics of moderate response and large storage capacity. The moderate fluctuated power of the renewable energy is compensated by a trend forecasting method with the Artificial Neural Network. In case of excess of the power generation by the renewable energy to demand it is converted to hydrogen with EL. In contrast, shortage of the electric power is made up with FC. The faster fluctuation power that cannot be compensated by the forecasting method is effectively compensated by SMES. In the ASPCS, the SMES coil with an MgB2 conductor is operated at 20 K by using liquid hydrogen supplied from a liquid hydrogen tank of the fuel cell vehicle station. The necessary storage capacity of SMES is estimated as 50 MJ to 100 MJ depending on the forecasting time for compensating fluctuation power of the rated wind power generation of 5.0 MW. As a safety case, a thermosiphon cooling system is used to cool indirectly the MgB2 SMES coil by thermal conduction. In this paper, a trend forecasting result of output power of a wind power generation and the estimated storage capacity of SMES are reported.

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model’s prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

Forecasting Monsoon Precipitation Using Artificial Neural Networks

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It presents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corresponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

Artificial-neural-network-based storage method for three-dimensional temperature field data during friction stir welding

In this paper,a new storage method for the three-dimensional temperature field data based on artificial neural network(ANN)was proposed.A multilayer perceptron that takes the coordinate(x,y,z)as inputs and temperature T as output,is used to fit the three-dimension-al welding temperature field.Effect of number of ANN layers and number of neurons on the fitting errors is investigated.It is found that the errors decrease with the number of hidden layers and neural numbers per layers generally.When the number of hidden layers increases from 1 to 6,the maximum temperature error decreases from 74.74℃to less than 2℃.The three-dimensional temperature field data is obtained by finite element simulation,and the experimental verification is completed by comparing the simulation peak temperatures with the measured results.As an example,an ANN with 4 hidden layers and 12 neurons in each layer were applied to test the performance of the proposed method in storage of the three-dimensional temperature field data during friction stir welding.It is found that the average error between the temperature data stored in ANN and the original simulation data that stored point-by-point is 0.517℃,and the error on the maximum temper-ature is 0.193℃,while the occupied disk space is only 0.27%of that is required in the conventional point-by-point storage.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

An Artificial Neural Network Approach for Credit Risk Management

The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.

Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.

Flux-measuring approach of high temperature metal liquid based on BP neural networks

A soft-measuring approach is presented to measure the flux of liquid zinc with high temperature andcausticity. By constructing mathematical model based on neural networks, weighing the mass of liquid zinc, the fluxof liquid zinc is acquired indirectly, the measuring on line and flux control are realized. Simulation results and indus-trial practice demonstrate that the relative error between the estimated flux value and practical measured flux value islower than 1.5%, meeting the need of industrial process.

Application of Artificial Network in Forecasting Liquefaction of Saturated Sandy Soil

The forecasting of liquefaction of saturated sand is difficult because of its complexity, but it is of importance in practice. The technique of artificial neural network is used to model the forecasting of liquefaction phenonmenan. The dimension analysis is processed, based on which a concise architecture of BP network is built then. It is shown that artificial neural network is an effective tool in forecasting of liquefaction.

Artificial neural network modeling of mechanical properties of armor steel under complex loading conditions

An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The tempera- ture and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.