Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING

Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.

Research on runoff variations based on wavelet analysis and wavelet neural network model： A case study of the Heihe River drainage basin （1944-2005）

The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.

Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model

In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL

Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast.

Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons:A Combined PCA and BP Neural Network Model

This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.

RECURRENT NEURAL NETWORK MODEL BASED ON PROJECTIVE OPERATOR AND ITS APPLICATION TO OPTIMIZATION PROBLEMS

The recurrent neural network （RNN） model based on projective operator was studied. Different from the former study, the value region of projective operator in the neural network in this paper is a general closed convex subset of n-dimensional Euclidean space and it is not a compact convex set in general, that is, the value region of projective operator is probably unbounded. It was proved that the network has a global solution and its solution trajectory converges to some equilibrium set whenever objective function satisfies some conditions. After that, the model was applied to continuously differentiable optimization and nonlinear or implicit complementarity problems. In addition, simulation experiments confirm the efficiency of the RNN.

Designing and optimizing a parallel neural network model for predicting the solubility of diosgenin in n-alkanols

Accurate estimation of the solubility of a chemical compound is an important issue for many industrial proce sses.To overcome the defects of some thermodynamic models and simple correlations,a parallel neural network(PNN) model was conceived and optimized to predict the solubility of diosgenin in seven n-alkanols(C_(1)-C_(7)).The linear regression analysis of the parity plots indicates that the PNN model can give more accurate descriptions of the solubility of diosgenin than the ordinary neural network(ONN) model.The comparison of the average root mean square deviation(RMSD) shows that the suggested model has a slight advantage over the thermodynamic NRTL model in terms of the calculating precision.Moreover,the PNN model can reflect the effects of the temperature and the chain length of the alcohol solvent on the solution behavior of diosgenin correctly and can estimate its solubility in the n-alkanols with more carbon atoms.

Interval standard neural network models for nonlinear systems

A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design ap- proach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

Artificial Neural Network Model for Discrimination of Stability of Ancient Landslide in Impounding Area of Three Gorges Project, China

The factors of geomorphology, geological setting, effect of ground water and environment dynamic factors (e.g. rainfall and artificial water recharge) should be integrated in the discrimination of the stability of the ancient landslide. As the criterion of landslide stability has been studied, the artificial neural network model was then applied to discriminate the stability of the ancient landslide in the impounding area of the Three Gorges project on the Yangtze River, China. The model has the property of self adaptive identifying and integrating complex qualitative factors and quantitative factors. The results of the artificial neural network model are coincided well with what were gained by classical limit equilibrium analysis (the Bishop method and Janbu method) and by other comprehensive discrimination methods.

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.

Artificial neural network model of constitutive relations for shock-prestrained copper

Data from the deformation on Split Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5 , the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1 , and the accuracy of the rate error is less than 3%. [

A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

Batch Process Modelling and Optimal Control Based on Neural Network Model

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELING TO PLASMA ARC WELDING OF ALUMINUM ALLOYS

By using alternating current plasma arc welding,the influences were studied of such parameters as welding curent,arc voltage,welding speed,wire feed rate,and magnitude of ion gas flow on front melting width,wdle reinforcement,and back melting width of LF6 aluminum alloy.Model of the formation of welding seam in alternating current plasma arc welding of aluminum was set up with the method of artificial neural neural network - BP algorithm. Qyakuty of formation was consequently predicted and evaluated.The experimental result shows that,compared with other modeling methods,artificial network model can be used to more accurately predict formation of weld,and to guide the production practice.

Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India

Estimation of petrophysical parameters is an important issue of any reservoirs. Porosity, volume of shale and water saturation has been evaluated for reservoirs of Upper Assam basin, located in northeastern India from well log and seismic data. Absolute acoustic impedance(AAI) and relative acoustic impedance(RAI) are generated from model based inversion of 2-D post-stack seismic data. The top of geological formation, sand reservoirs, shale layers and discontinuities at faults are detected in RAI section under the study area. Tipam Sandstone(TS) and Barail Arenaceous Sandstone(BAS) are the main reservoirs,delineated from the logs of available wells and RAI section. Porosity section is obtained using porosity wavelet and porosity reflectivity from post-stack seismic data. Two multilayered feed forward neural network(MLFN) models are created with inputs: AAI, porosity, density and shear impedance and outputs: volume of shale and water saturation with single hidden layer. The estimated average porosity in TS and BAS reservoir varies from 30% to 36% and 18% to 30% respectively. The volume of shale and water saturation ranges from 10% to 30% and 20% to 60% in TS reservoir and 28% to 30% and 23% to 55% in BAS reservoir respectively.

ARTIFICIAL NEURAL NETWORK MODELLING OF A WOOD CHIP REFINER

1 INTRODUCTIONWood chip refining is the most critical step in mechanical pulping.Commercical experi-ences have been gained for years.Modelling and control of chip refiners,however,pose a challenge mainly because of the stochastic nature of the process.Some attemptshave been made to employ factor analysis technique[1]in the modelling andsimulating of refiner operation[2,3].Strand[2]used common factors as links betweenintrinsic fibre properties and pulp quality.He believed that a qualitative concept onthe physical nature of these common factors could be arrived at,and thus would helpto understand what refining variables need to be controlled or adjusted in order to im-prove pulp quality.However,the linear model used in factor analysis is based on theassumption that the interactions among the system variables are linear,which,ofcourse,is not true in practice.