Artificial Neural Network Model For Prediction Research Articles

Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy

We have developed an artificial neural network prediction model for end-stage kidney disease (ESKD) in patients with primary immunoglobulin A nephropathy (IgAN) using a retrospective cohort of 948 patients with IgAN. Our tool is based on a two-step procedure of a classifier model that predicts ESKD, and a regression model that predicts development of ESKD over time. The classifier model showed a performance value of 0.82 (area under the receiver operating characteristic curve) in patients with a follow-up of five years, which improved to 0.89 at the ten-year follow-up. Both models had a higher recall rate, which indicated the practicality of the tool. The regression model showed a mean absolute error of 1.78 years and a root mean square error of 2.15 years. Testing in an independent cohort of 167patients with IgAN found successful results for 91% of the patients. Comparison of our system with other mathematical models showed the highest discriminant Harrell C index at five- and ten-years follow-up (81% and 86%, respectively), paralleling the lowest Akaike information criterion values (355.01 and 269.56, respectively). Moreover, our system was the best calibrated model indicating that the predicted and observed outcome probabilities did not significantly differ. Finally, the dynamic discrimination indexes of our artificial neural network, expressed as the weighted average of time-dependent areas under the curve calculated at one and two years, were 0.80 and 0.79, respectively. Similar results were observed over a 25-year follow-up period. Thus, our tool identified individuals who were at a high risk of developing ESKD due to IgAN and predicted the time-to-event endpoint. Accurate prediction is an important step toward introduction of a therapeutic strategy for improving clinical outcomes.

Prediction of hydrogen-added combustion process in T-GDI engine using artificial neural network

This study investigates the prediction of the combustion process using an artificial neural network (ANN), and an efficient prediction methodology is introduced. In particular, the conditions for using hydrogen as an additive to a turbo-charged gasoline direct injection (T-GDI) engine are discussed. Research to predict the physical phenomena using ANNs has been actively conducted for various applications, including internal combustion engines. However, a large amount of data must be collected under various conditions to establish these predictions. Furthermore, the prediction of complex phenomena such as engine-combustion processes mandates data collection under diverse conditions. It is therefore very costly and time-consuming to obtain these data experimentally under a wide range of conditions. However, the methodology introduced in this study can enable effective prediction of complex combustion processes, such as hydrogen-added combustion, with minimal experimental data. To implement this methodology, the target engine was modeled using commercial 1D engine-simulation software GT-Power based on certain experimental results obtained under select conditions. The data for the ANN training under an expanded range of conditions were obtained using the GT-Power engine model. According to the obtained data, the ANN model for prediction of the hydrogen-added combustion processes in the T-GDI engine was constructed, and its results were compared with the experimental results. A reasonable agreement between the compared results was observed, which demonstrated the validity and reliability of the prediction model. The constructed ANN combustion model has the potential that it can be applied to transient conditions or used as a virtual sensor, unlike general combustion models, and this study presented an economical and efficient way to build such a model.

Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves

In this paper, artificial neural network (ANN) models are developed to predict the load-displacement curves for better understanding the behavior of cotton fiber/polyvinyl chloride (PVC) composites. Series of experiments were undertaken in the laboratory for a varying percentage of composite fiber to characteristic loading. Based on those experimental data, the ANN models were trained and tested on the TensorFlow backend using Keras library in Python by implementing the back-propagation method. For better prediction and accuracy of the load-displacement curves, the grid search hyperparameter tuning method was used, followed by k-fold cross-validation. The developed approach proved to be very efficient and reduced the time and effort of the behavioral study for numerous samples, and it will help materials designers to design their future experiments effectively. A similar approach to predict load-displacement curves using ANN can be extended for any kind of composite material if the necessary experimental data are available.

Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study

Artificial Neural Networks (ANN) has been well studied for flood prediction. However, there is not enough empirical evidence to generalize ANN applicability to small countries with microclimates prevailing in a small geographical space. In this paper, we focus on the climatic conditions of Mauritius for which we seek to investigate the accuracy of using ANN to predict flooding using locally collected data from 11 meteorological stations spread across the country. The ANN model for flood prediction presented in this work is trained using 20,000 climate data records, collected over a period of two years for Mauritius. Our input climate features are minimum temperature, maximum temperature, rainfall and humidity and our output decision is „flood‟ or „no flood‟. Using ANN, we achieved an accuracy of 98% for flood prediction and hence, we conclude that ANN is indeed a good predictor for flood occurrence even for regions with predominantly microclimatic conditions.

Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds

Rapid, non-destructive and reliable detection of starch content in single seed is significant to facilitate the breeding of high-starch corn but difficult for a traditional method of seed composition analysis. This study investigated the possibility of using near-infrared (NIR) hyperspectral imaging technology to determine the starch content in a single kernel corn seed. The hyperspectral images including embryo-up and embryo-down orientations of a corn seed were acquired with a range of 930–2500 nm. The characteristic spectrum of each corn seed was calculated by averaging the two sides’ spectra. All spectra were preprocessed by the smoothing and derivative algorithm, and then, the characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) method. The selected wavelengths were used as the inputs to develop partial least squares regression (PLSR) and nonlinear statistical data models with artificial neural networks (ANN) algorithm. The results indicated that the ANN prediction model based on Levenberg-Marquardt algorithm (LMA) was the optimal for starch content determination with correlation coefficient (Rp) of 0.96 and root mean square error of prediction (RMSEP) of 0.98 in prediction sets. Therefore, NIR hyperspectral imaging technology combined with appropriate chemometric analysis can be considered as a useful tool for starch content determination in corn seed at a kernel level. These results can provide a useful reference for rapid and non-destructive detection of other chemical composition in single corn seed.

Application of Artificial Neural Network for Prediction of Key Indexes of Corn Industrial Drying by Considering the Ambient Conditions

Uncontrollable ambient conditions are the main factors limiting the self-adaption control of an industrial drying system. To achieve the goal of accurate control of the drying process, the influence of the ambient conditions on the drying behavior should be taken into consideration when modeling the drying process. Present work introduced an industrial drying system with a loading capacity of 50 t, two artificial neural network prediction models with (IANN) and without (OANN) considering the ambient conditions were established using artificial neural network modeling approach. The ambient conditions on the moisture content (MC), exergy efficiency of the heat exchanger (ηex,h) and specific recovered radiant energy (Er) of the drying process were also investigated. The results showed that the ηex,h and Er increase with the increase of ambient temperature while the drying time decrease with the increase of the ambient temperature. The IANN model has a better prediction performance that that of OANN model. An optimal architecture of 9-2-12-3 artificial neuron network model was developed and the best prediction performance of the artificial neural network (ANN) model were found at a training epoch number of 30, and a momentum coefficient of 0.4, where the coefficient of determination of moisture content, exergy efficiency of heat exchanger, and the specific recovered radiant energy, respectively are 0.998, 0.992, and 0.980, indicating that the model has an excellent prediction performance and can be used in engineering practice.

Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation

To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models are required for more precise prediction of the amount and recovery time of methane gas from landfills. When the study associates to greenhouse gas emissions problems, time series prediction models are of considerable interests, in which significant past records of gas data are required. This study is the first to specially impute the missing methane (CH4) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH4 generation rate from a landfill in Regina, SK, Canada. Pre-processing was conducted on data to evaluate independent and significant meteorological input variables and provide suitable dataset for developing CH4 generation models. A two-stage time series model proposed in this study was performed by missing data imputation at the first stage, followed by a neural network auto-regressive model with exogenous inputs (NARX) at the second stage. The model with 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal structure. This model shows the high performance in CH4 prediction with the average index of agreement of 0.92 and the average mean absolute percentage error (MAPE) of 3.03% during the testing stage. Missing data imputation coupled with NARX method decreased the mean squared error (MSE) of the model by 84% (compared to Multilayer Perceptrons neural network model) in the testing period representing the effectiveness of missing data estimation coupling with time series ANN models in daily CH4 generation prediction.

Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting

Drought prediction is an important subject, particularly in drought-hydrology, and has a key role in risk management, drought readiness and alleviation. Hydrological time series data consists of nonlinear features and various time scales. With this view in mind, this study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to test a new method of a hybrid model for their ability to accurately predict future droughts. A 30-year rainfall data from the year 1986 to 2016 for Malaysia’s Langat River Basin was analyzed. Meteorological drought indices (DI) such as the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP) were used to compute historical drought events. At first, each of these computed drought time series went through a process of decomposition to be divided as low frequency and high-frequency sub-series by discrete wavelet transform (DWT). Secondly, the high and low-frequency sub-series were passed through the predictive model of ANN and ARIMA techniques, respectively. Lastly, the predicted sub-series were used to reconstruct and develop a final drought prediction model. It was found that the Wavelet-ARIMA-ANN (which named as W-2A) model outperformed the single ANN and wavelet-ANN predictive models. The ANN model developed by SPI achieved an overall correlation co-efficient R-value of 0.423, but the wavelet-based ANN model decreased in the R-value to 0.415. Finally, two different models, which were established using drought indices SPI and SIAP, and discrete wavelet transformation-based hybrid ANN-ARIMA (W-2A), have achieved improved R values of 0.914 and 0.934 respectively.

Dataset and ANN model prediction of performance of graphene nanolubricant with R600a in domestic refrigerator system.

This work evaluated the steady state performance of R600a in the base lubricant and graphene nanolubricant. The measuring instruments required and their uncertainties were provided, step by step method and procedures for preparation of graphene nanolubricant concentration and substituting it with the base lubricant in domestic refrigerator system are described. The system temperatures data was captured at the inlet and outlet of the system components. Also, the pressures data was recorded at the compressor inlet and outlet. The data was recorded for 3 h at 30 min interval at an ambient temperature of 27 °C. The experimental dataset, Artificial Neural Network (ANN) training and testing dataset are provided. The artificial intelligence approach of ANN model to predict the performance of graphene nanolubricant in domestic refrigerator is explained. Also, the ANN model prediction statistical performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2) are also provided. The data is useful to researchers in the field of refrigeration and energy efficiency materials, for replacing nanolubricant with the base lubricant in refrigerator systems. The data can be reuse for simulation and modelling vapour compression energy system.

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

The use of surrounding rock loosening circle theory combined with elastic-plastic mechanics calculation method and depth learning in roadway support.

The objective is to study the design method of roadway support and provide technical support for coal mining and other mining methods that need deep roadway excavation. Through literature review, the occurrence, development mechanism and influencing factors of surrounding loose rock zones of roadways are analyzed. A method of detecting is selected according to the characteristics of surrounding rock loosening. Knowledge of elastoplastic mechanics is used to theoretically study the failure mechanism of surrounding rock in deep re-mining roadways. Based on the artificial neural network prediction model (ANN), the surrounding rock is classified and a support network model of the decision system is constructed. After the design of roadway support, a sharp change of vault subsidence normally occurs within about 7 days after excavation, and the total subsidence is 14 mm. In the following month, deformation is slow, subsidence is small, and it is basically stable after one month. The curve of the vault subsidence rate shows that the vault subsidence rate is less than or equal to 1mm/d after 7 days. The convergence rate is also less than 1 mm/d after 7 days. There are two cave mouths AB and CD, and the convergence value of AB is 6.47mm, CD is 10.26mm: CD is slightly larger than AB, and it is close to stable one month later. It is essentially consistent with the time of vault settlement and stability, and the amount of deformation is approximately the same. This shows that, with the advance of the working face, the displacement of the surrounding rock in the section away from the working face will gradually decrease and the surrounding rock will be stable. The stability time of surrounding rock displacement of the portal section is the same as that of vault subsidence under the initial support, and the amount of deformation is approximately the same, indicating that the support parameters and construction methods are reasonable.

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study.

BackgroundRecently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship between clinicopathologic variables and survival for patients with gastric cancer. Using a multinational cohort, this study aimed to develop and validate an ANN‐based survival prediction model for patients with gastric cancer.MethodsPatients with gastric cancer who underwent gastrectomy in a Chinese center, a Japanese center, and recorded in the Surveillance, Epidemiology, and End Results database, respectively, were included in this study. Multilayer perceptron neural network was used to develop the prediction model. Time‐dependent receiver operating characteristic (ROC) curves, area under the curves (AUCs), and decision curve analysis (DCA) were used to compare the ANN model with previous prediction models.ResultsAn ANN model with nine input nodes, nine hidden nodes, and two output nodes was constructed. These three cohort's data showed that the AUC of the model was 0.795, 0.836, and 0.850 for 5‐year survival prediction, respectively. In the calibration curve analysis, the ANN‐predicted survival had a high consistency with the actual survival. Comparison of the DCA and time‐dependent ROC between the ANN model and previous prediction models showed that the ANN model had good and stable prediction capability compared to the previous models in all cohorts.ConclusionsThe ANN model has significantly better discriminative capability and allows an individualized survival prediction. This model has good versatility in Eastern and Western data and has high clinical application value.

Development and application of ANN model for property prediction of supercritical kerosene

Three artificial neural network (ANN) models were developed to predict the fluid properties of China RP-3 kerosene under supercritical pressure in replacement of the time-consuming property calculations by the principle of Extended Corresponding State (ECS). The analysis shows that the properties predicted by the trained ANN models agree well with the calculations by the ECS method. The correlation coefficients (R) between the ANN predictions and the ECS calculations are higher than 0.99, and most of the relative errors are lower than 0.1%. The prediction by the ANN models is of several orders (104) faster than that by the ECS method, especially near the critical points. The trained ANN model was further coupled with the CFD modeling of a realistic kerosene jet, where high efficiency and satisfactory accuracy were shown compared with the direct ECS calculations.

Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN

The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates the goodness of fit for the model and high significance of the model. From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly. However, if the test patterns number will be increased then this error can be further minimized. The proposed RSM and ANN prediction model sufficiently predict Ra accurately. However, ANN prediction model is found to be better compared to RSM model. The artificial neutral network is applied to experimental results to find prediction results for two response parameters. The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which produces flexibility to the manufacturing industries to select the best setting based on applications.

Optimal price based control of HVAC systems in multizone office buildings for demand response

Optimizing the scheduling of heating, ventilation, and air-conditioning (HVAC) systems in multizone buildings is a challenging task, as occupants in various zones have different thermal preferences dependent on time-varying indoor and outdoor environmental conditions and price signals. Price-based demand response (PBDR) is a powerful technique that can be used to handle the aggregated peak demand, energy consumption, and cost by controlling HVAC thermostat settings based on time-varying price signals. This paper proposes an intelligent and new PBDR control strategy for multizone office buildings fed from renewable energy sources (RESs) and/or utility grid to optimize the HVAC operation considering the varying thermal preferences of occupants in various zones as a response of real-time pricing (RTP) signals. A detailed mathematical model of a commercial building is presented to evaluate the thermal response of a multizone office building to the operation of an HVAC system. The developed thermal model considers all architectural and geographical effects to provide an accurate calculation of the HVAC load demand for analyses. Further, Occupants’ varying thermal preferences represented as a coefficient of a bidding price (chosen by the occupants) in response to price signals are modeled using an artificial neural network (ANN) and integrated into the optimal HVAC scheduling. Furthermore, a control mechanism is developed to determine the varying HVAC thermostat settings in various zones based on the ANN prediction model results. The effect of the proposed strategy on aggregator utility with wider implementation of the developed mechanism is also considered. The optimization problem for the proposed PBDR control strategy is formulated using a building’s thermal model and an occupant’s thermal preferences model, and simulation results are obtained using MATLAB/Simulink tool. The results indicate that the proposed strategy with realistic parameter settings shows a reduction in peak demand varying from 7.19% to 26.8%, contingent on the occupant’s comfort preferences in the coefficient of the bidding price compared to conventional control. This shows that the proposed approach successfully optimizes the HVAC operation in a multizone office building while maintaining the preferred thermal conditions in various zones. Moreover, this technique can help in balancing the energy supply and demand due to the stochastic nature of RESs by cutting electricity consumption.

A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information.

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.

Comparison of the artificial neural network model prediction and the experimental results for cutting region temperature and surface roughness in laser cutting of AL6061T6 alloy

In this study, a function approximation procedure is used, which called artificial neural network (ANN), according to the experimental results of the temperature of cutting region and surface roughness in cutting by the laser of AL6061T6 alloy. The cutting speed, laser power, sheet thickness, and assistant gas pressure as the inputs parameters and the surface roughness and cutting temperature as the target attributes are considered. The novelty of this study is shown by preparing 30 unalike ANN procedures to propose suitable architectures and training algorithms for them. The results of ANN are compared with the experimental results, and the error percent between them is derived. According to the comparison, the error percent between the experimental data and ANN is in a reasonable range, and this numerical method can be applied with low times and costs. For the cutting temperature, the mean value of the error percentages between the numerical results and experimental data is 0.66%. Also, for the surface roughness, the mean value of the error percentages is 5.79%.

A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

New insights into regional differences of the predictions of municipal solid waste generation rates using artificial neural networks

As one of the most popular non-linear models, artificial neural network (ANN) has been successfully applied in the prediction of municipal solid waste (MSW). Despite its high accuracy achieved in a specific city or region, little progress is made on a larger-scale, which would be resulted from the regional difference. In this study, ANN models for MSW prediction in mainland China are developed and optimized. Besides a model aiming for all cities, regional models are developed by grouping these cities into three categories. Impact of regional difference in MSW prediction is analyzed by evaluation of model’s dependence on each predictor, and comparisons made between these models. Results show that regional difference has huge impact on MSW prediction. Accuracy of MSW prediction would increase from 0.916 in R2 and 59.3 in rooted mean squared error (RMSE) to 0.968/0.946/0.943 in R2 and 6.4/9.7/17.6 in RMSE for southern/northern/western region after a three-region division. Models for MSW prediction in southern and northern region of mainland China share much similarity in dependence on predictors, which differs a lot from that for western region. Further cross-prediction process confirmed that models for southern or northern regions might be suitable for the MSW prediction in another, yet not apply to that in western region. Such large-scale based model can be used by cities lacking historical data for prediction of their local MSW generation, the predictive result would be helpful in MSW disposal planning and the analysis of regional difference would be helpful in establishing regional policy, especially for the three regions in mainland China.

Formula-E race strategy development using artificial neural networks and Monte Carlo tree search

Energy management has been one of the most important parts in electric race strategies since the Fédération Internationale de l’Automobile Formula-E championships were launched in 2014. Since that time, a number of unfavorable race finishes have been witnessed due to poor energy management. Previous researches have been focused on managing the power flow between different energy sources or different energy consumers based on a fixed cycle. However, there is no published work in the literature about energy management of a full electric racing car on repeated course but with changeable settings and driving styles. Different from traditional energy management problems, the electric race strategy is more of a multi-stage decision-making problem which has a very large scale. Meanwhile, this is a time-critical task in motorsport where fast prediction tools are needed and decisions have to be made in seconds to benefit the final outcome of the race. In this study, the use of artificial neural networks (ANN) and tree search techniques is investigated as an approach to solve such a large-scale problem. ANN prediction models are developed to replace the traditional lap time simulation as a much faster performance prediction tool. Implementation of Monte Carlo tree search based on the proposed ANN fast prediction models has provided decent capability to generate decision-making solution for both pre-race planning and in-race reaction to unexpected scenarios.