Back Propagation Genetic Algorithm Research Articles

Heuristic prediction of gas precipitation performance of self-excited oscillation cavity

The precipitation of dissolved gas in oil is a challenging problem in pollution control of hydraulic systems. When the self-excited oscillation jet is formed, there are two low-pressure regions in the self-excited oscillation cavity, and the reduction in pressure causes the dissolved gas in the oil to precipitate out. Here, we investigated the effect of the self-excited oscillation cavity on the dissolution of dissolved gas in oil. We studied the gas precipitation performance of the self-excited oscillation cavity by simulating the pressure and velocity fields inside the cavity under different ratios of dimensionless structure parameters. The results indicated that parameter intervals for maintaining good gas precipitation performance of the self-excited oscillation cavity were d2/d1=2–2.4, D/d2=4–6, and D/L = 2. We then used a heuristic prediction algorithm (Genetic algorithm-backpropagation, GA-BP) to fit the simulation and experimental data, in which the root mean square error between the simulation and experimental data was only 2.45%. This indicated that the simulation of the flow field was reasonable, and that the GA-BP model performed well in predicting the gas precipitation performance of the self-excited oscillation cavity. Our results have important guiding significance for future studies on the gas precipitation performance of the self-excited oscillation cavity.

Prediction of the roughness coefficient for drainage pipelines with sediments using GA-BPNN.

Accurate prediction of the roughness coefficient of sediment-containing drainage pipes can help engineers optimize urban drainage systems. In this paper, the variation of the roughness coefficient of circular drainage pipes containing different thicknesses of sediments under different flows and slopes was studied by experimental measurements. Back Propagation Neural Network (BPNN) and Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) were used to predict the roughness coefficient. To explore the potential of artificial neural networks to predict the roughness coefficient, a formula based on drag segmentation was established to calculate the roughness coefficient. The results show that the variation trend of the roughness coefficient with flow, hydraulic radius, and Reynolds number is consistent. With the increase of the three parameters, the roughness coefficient decreases overall. Compared to the traditional empirical formula, the BPNN model and the GA-BPNN model increased the determination factors in the testing stage by 3.47 and 3.99%, respectively, and reduced the mean absolute errors by 41.18 and 47.06%, respectively. The study provides an intelligent method for accurate prediction of sediment-containing drainage pipes roughness coefficient.

Energy finance risk warning model based on GABP algorithm

Energy finance is the product of the close combination of the energy industry and the financial industry, and the two affect each other. The energy crisis may lead to a financial crisis, and the financial crisis may also lead to a energy crisis. Early risk warning for the energy financial crisis can effectively mitigate and reduce risks. This article used the GABP (Genetic Algorithm Back Propagation) algorithm model to systematically analyze and predict the risks of energy financial crises. After establishing indicators for energy finance risk warning, this article collected relevant data from 150 energy companies and 210 financial companies, and compared them with the GABP algorithm model and manual analysis model. The error value of the model is determined by the numerical expansion in the positive and negative directions based on zero scale values. The closer the zero scale value is, the smaller the error; the farther it is from the zero scale value, the greater the error. The results show that the average accuracy of the GABP model for energy finance risk warning is 85.2%, and the minimum error value is −0.23. The average accuracy of using manual analysis models for energy finance risk warning is 75.8%, with a minimum error value of 1.89. The GABP algorithm has advantages in constructing energy finance risk warning models.

Application of GA-BP in Displacement Force Inverse Analysis and Mechanical Parameter Inversion of Deep Foundation Pits

Aiming at the defects of various existing displacement inverse analysis methods, using the nonlinear mapping ability of neural network and the global random search ability of genetic algorithm, this paper proposes a displacement inverse analysis method based on optimized Genetic Algorithm- Back Propagation (GA-BP) for deep foundation pit support. The method changes the method that BP algorithm relies on the guidance of gradient information to adjust the network weights, but uses the characteristics of global search of genetic algorithm to find the most suitable network connection rights and network structure, etc. to achieve the purpose of optimization. Firstly, the deformation mechanism of deep foundation pit is analyzed, its failure mode is summarized, and the calculation method of lateral rock and soil pressure is sorted out according to the code. The theory and characteristics of BP neural network and genetic algorithm are discussed, and the method of using genetic algorithm to optimize BP neural network is proposed to improve the prediction accuracy. In view of the shortcomings of GA-BP neural network prediction model in training sample pretreatment and hidden layer structure design, the optimal normalization interval was determined by correlation coefficient regression analysis, and the analytical expression of the number of neurons in hidden layer was derived by statistical principle, and the value range of the optimal number of neurons in single hidden layer was proposed. Combined with the actual engineering, the mechanical parameters inversion and displacement force inverse analysis are performed using this method, and the results show that the optimized GA-BP has higher prediction accuracy compared with BP neural network and GA-BP, and the deviation of the displacement prediction value at each depth is kept within 0.2 mm, the absolute error interval width is 0.07 mm, and the maximum relative error is 1.35% at 4.0 m depth.

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

AbstractTo address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.

Optimization of adsorption performance of cerium-loaded intercalated bentonite by CCD-RSM and GA-BPNN and its application in simultaneous removal of phosphorus and ammonia nitrogen

Excessive phosphorus (P) and ammonia nitrogen (NH3–N) in water bodies can lead to eutrophication of the aquatic environment. Therefore, it is important to develop a technology that can efficiently remove P and NH3–N from water. Here, the adsorption performance of cerium-loaded intercalated bentonite (Ce-bentonite) was optimized based on single-factor experiments using central composite design-response surface methodology (CCD-RSM) and genetic algorithm-back propagation neural network (GA-BPNN) models. Based on the determination coefficient (R2), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE), the GA-BPNN model was found to be more accurate in predicting adsorption conditions than the CCD-RSM model. The validation results showed that the removal efficiency of P and NH3–N by Ce-bentonite under optimal adsorption conditions (adsorbent dosage = 1.0 g, adsorption time = 60 min, pH = 8, initial concentration = 30 mg/L) reached 95.70% and 65.93%. Furthermore, based on the application of these optimal conditions in simultaneous removal of P and NH3–N by Ce-bentonite, pseudo-second order and Freundlich models were able to better analyze adsorption kinetics and isotherms. It is concluded that the optimization of experimental conditions by GA-BPNN has some guidance and provides a new approach to explore adsorption performance after optimizing the conditions.

Optimization path of corporate ESG performance and compensation performance management under carbon neutral target

Abstract Under the development of low carbon economy with the goal of “carbon neutrality”, high-emission enterprises are facing more severe pressure of energy saving and emission reduction, and how to build and corporate payroll performance management system becomes the key to corporate carbon compliance. And in the green finance-assisted green low-carbon sustainable development has become the development direction, (Environmental-Social-Governance, ESG) evaluation system has also increasingly highlighted its importance, the ESG evaluation system as the leading investment concept in the international has been the mainstream. This paper analyzes the problems of enterprise compensation management system, constructs an optimization model of enterprise compensation management based on Genetic Algorithm-Back Propagation (GA-BP) neural network, and proposes measures to optimize enterprise compensation in the context of ESG performance, taking into account the current international enterprise economic policy background. The study proposes policy recommendations to promote the implementation of green and sustainable development concepts and policies during the 14th Five-Year Plan period, and effectively addresses the adaptation of ESG and corporate compensation management under the goal of carbon neutrality.

Optimal urban competitiveness assessment using cloud computing and neural network

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively.

A machine learning method for HTLCF life prediction of titanium aluminum alloys with consideration of manufacturing processes

Most conventional methods only consider the effects of materials and loading conditions when predicting the high-temperature low-cycle fatigue life of titanium aluminum alloys. However, the effects of manufacturing processes are not considered. To address this limitation, this paper proposes a machine learning method. First, the loading conditions are preliminarily filtered by physics knowledge. Second, the manufacturing processes and loading conditions are analyzed by using the average value of multiple mean impact values. Then, the variables that have little contribution to the output are filtered out, and the remaining variables are used as inputs. Finally, an optimized genetic algorithm-back propagation artificial neural network is established to predict the low-cycle fatigue life of the materials. The optimization methods are Levy flight and an adaptive adjustment mechanism of probability. Min-Max normalization is used to normalize and denormalize the data in the proposed method. A set of experimental data for Ti-685 is used to validate the proposed method. Moreover, the results show that the proposed method has better prediction accuracy than other methods.

Structural optimization of multistage centrifugal pump via computational fluid dynamics and machine learning method

Abstract To implement energy savings in multistage centrifugal pumps, a return channel is utilized to replace the origin inter-stage flow channel structure, and then a single-objective optimization work containing high-precision numerical simulation, design variable dimensionality reduction, and machine learning is conducted to obtain the optimal geometric parameters. The variable dimensionality reduction process is based on the Spearman correlation analysis method. The influence of 15 design variables of the impeller and return channel is investigated, and seven of them with high-impact factors are selected as the final optimization variables. Thereafter, a genetic algorithm-backpropagation neural network (GA-BPNN) model is used to create a surrogate model with a high-fitting performance by employing a GA to optimize the initial thresholds and weights of a BPNN. Finally, a multi-island genetic algorithm (MIGA) is employed to maximize hydraulic efficiency under the nominal condition. The findings demonstrate that the optimized model’s efficiency is increased by 4.29% at 1.0Qd, and the deterioration of the pump performance under overload conditions is effectively eliminated (the maximum efficiency increase is 14.72% at 1.3Qd). Furthermore, the internal flow analysis indicates that the optimization scheme can improve the turbulence kinetic energy distribution and reduce unstable flow structures in the multistage centrifugal pump.

Voids prediction beneath cement concrete slabs using a FEM-ANN method

ABSTRACT The voids beneath cement concrete slabs are a major invisible disease, resulting in a rapid decrease in service performance in the composite pavement. Accurate voids prediction is essential for the extensive application and long-term service of composite pavement. This research provides a FEM-ANN (Finite Element Modelling-Artificial Neural Network) method to predict the voids beneath concrete slabs. These ANN models include the original back propagation (BP), the particle swarm optimisation (PSO) BP model, the genetic algorithm (GA) BP model, and the whale optimisation algorithm (WOA) BP model. The voids FEM model is established and validated by the measured data in the field, and the relative error of measured and simulated results is within 4%. The cross-validation results show that the WOA-BP model has the best prediction performance, with the highest score of 8, which refers to the overall score of the mean value and variance of these evaluation indices. Therefore, this FEM-ANN framework is an efficient method for estimating the voids beneath concrete slabs. Furthermore, it is discovered that the base modulus with the highest contribution degree of 20.34% is the most dominant factor in predicting the voids output. HIGHLIGHTS A FEM-ANN method is utilised to predict the voids beneath concrete slabs The WOA-BP model exhibits the best comprehensive performance of the four ANN models. W d and pavement mechanical responses have a positive effect on A v opposite to K d and pavement structure. The base modulus is the primary factor in predicting the voids output.

Soil Moisture Inversion Based on Data Augmentation Method Using Multi-Source Remote Sensing Data

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm3/cm3, and a mean absolute error of 0.0422 cm3/cm3, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size.

Revealing and quantifying the effect of cattiite coprecipitation on the purity of K-struvite in aqueous solution

To investigate the effect of cattiite (Mg3(PO4)2·22 H2O) coprecipitate with K-struvite on the purity of K-struvite, K-struvite crystallization in aqueous solution was performed at different pHs and the initial Mg2+ concentrations. The coprecipitation was quantified by Simultaneous Thermal Analyzer, Scanning Electron Microscopy with energy-dispersive X-ray spectroscopy as well as Inductively Coupled Plasma Mass Spectrometer. Genetic algorithm-back propagation artificial neural network model (GA-BPANN) was also innovatively employed to simulate and predict K-struvite purity. The results revealed that cattiite coprecipitated with K-struvite rather than bobierrite (Mg3(PO4)2·8 H2O) in the solution. The low initial Mg2+ concentration and high pH were more conducive to K-struvite crystallization, thereby inhibiting the cattiite co-precipitation with K-struvite. Specifically, K-struvite crystals with the purity of 93.88% were achieved at pH 11 and initial Mg2+ concentration of 3 mmol/L, whereas cattiite crystals with the purity of 98.99% were observed at pH 9 and the initial Mg2+ concentration of 50 mmol/L. Notably, K-struvite and cattiite crystals have obvious differences in morphology and particle size, separately exhibiting small rod-like and large plate-like structures. Interestingly, GA-BPANN accurately simulated and predicted K-struvite purity at various pHs and initial Mg2+ concentrations. The findings herein provide a theoretical foundation for obtaining high-purity K-struvite from waste streams at industrial scale, which also provide a reference for exploring the coprecipitation of multiple minerals in waste streams.

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

Negative Effect Prediction and Refueling Traffic Flow Equilibrium of Urban Energy Supply Network during Weekends and Holidays

With the continuous improvement of people’s living standards, the travel demand for vehicles increases rapidly during weekends and holidays. This situation leads to a number of negative impacts on the transport network, such as traffic congestion, carbon emissions, and long queues at refueling stations. The impact of vehicles requiring energy replenishment on the negative effect is nonlinear. Therefore, the negative effect data of working days cannot be directly used to evaluate the cost, and the traffic equilibrium allocation problem also needs to be solved. An equilibrium allocation model of hybrid vehicles considering the energy replenishment demand in holidays is established, aiming at minimizing the total time cost and energy consumption cost. The Frank–Wolfe algorithm is used to solve the traffic allocation problem in advance, and the negative effect of the urban energy network during holidays is predicted by the Genetic Algorithm—Back Propagation (GA-BP) algorithm. Then, the energy-replenishing vehicles in the traffic network are induced to reduce the total cost. Finally, taking the actual road network in Jiulongpo District of Chongqing City, southwest of China, as an example, the negative effects of multiple stations are predicted. The results show that the prediction method proposed in this paper is effective during holiday periods. In addition, as the market share of electric vehicles increases, the negative cost can decrease gradually. The predicted results can provide a reference for traffic managers.

Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network

Since adhesive membrane fouling is critically determined by the interfacial interaction between a foulant and a rough membrane surface, efficient quantification of the interfacial interaction is critically important for adhesive membrane fouling mitigation. As a current available method, the advanced extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory involves complicated rigorous thermodynamic equations and massive amounts of computation, restricting its application. To solve this problem, artificial intelligence (AI) visualization technology was used to analyze the existing literature, and the genetic algorithm back propagation (GABP) artificial neural network (ANN) was employed to simplify thermodynamic calculation. The results showed that GABP ANN with 5 neurons could obtain reliable prediction performance in seconds, versus several hours or even days time-consuming by the advanced XDLVO theory. Moreover, the regression coefficient (R) of GABP reached 0.9999, and the error between the prediction results and the simulation results was less than 0.01%, indicating feasibility of the GABP ANN technique for quantification of interfacial interaction related with adhesive membrane fouling. This work provided a novel strategy to efficiently optimize the thermodynamic prediction of adhesive membrane fouling, beneficial for better understanding and control of adhesive membrane fouling.

New methods based on a genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting the occurrence of trihalomethanes in tap water

Monitoring trihalomethanes (THMs) levels in water supply systems is of great significance in ensuring drinking water safety. However, THMs detection is a time-consuming task. Developing predictive THMs models using parameters that are easier to obtain is an alternative. To date, there is still no application of optimization algorithms and general regression neural networks in predicting disinfection by-products levels. This study was to explore the feasibility of back propagation neural network (BPNN), genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting THMs occurrence in real water supply systems. The results showed that the BPNN models' prediction ability was limited (test rp = 0.571–0.857, N25 = 61.5 %–91.5 %). Optimized by the genetic algorithm (GA), GABP models were generated and exhibited better prediction performance (test rp = 0.573 and 0.696–0.863, N25 = 68.2 %–93.6 %). However, GABP models took a lot of time and their prediction performance was unstable. A GRNN was then used to generate simpler neural network models, and the resulting prediction performance was excellent (total trihalomethanes and bromodichloromethane: test rp = 0.657–0.824, N25 = 81.8 %–100 %). In general, GRNN was the best at predicting THMs concentrations among the three models. However, it is worth noting that the prediction accuracy of bromodichloromethane (BDCM) was not high, which may be due to the absence of key parameters affecting BDCM formation. Accurate predictions of THMs by GRNN with these nine water parameters made THMs monitoring in real water supply systems possible and practical.

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose.

Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

A new method to retrieve rainfall intensity level from rain-contaminated X-band marine radar image

ABSTRACT In this study, a new methodcombining wave-number energy spectrum (WES) and Genetic algorithm-back propagation neural network (GA-BPNN) isproposed to retrieve the rainfall intensity level from rain-contaminated X-bandmarine radar image. Since the intensity of spatialrainfall can be reflected by the distribution of energy in the wavenumberfrequency domain, the obtained WES is divided into three wavenumber segments(low, medium and high wavenumber segments), and the ratio of the wavenumber ineach wavenumber segment to the total wavenumber is calculated separately as the characteristicparameters. Based on the excellent networkconvergence speed and data prediction accuracy of the GA-BPNN, these calculatedparameters are input into the constructed GA-BPNN for training to complete thetask of rainfall intensity level retrieval. The proposed method is tested usingdata collected at the ocean observation station of Haitan Island in PingtanCounty. Referring to the actual rainfallintensity synchronously recorded by the rain gauge, the retrieval accuracy ofthe proposed method is 97.4%, which is 4.3% higher than that of back propagation neural network (BPNN) not optimizedby Geneticalgorithm(GA). In addition, compared with theretrieval performance of the ratio of zero intensity to echo (RZE) method basedon the occlusion area of radar image, the retrievalaccuracy of the proposed method is improved by about 12.9%.

Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks

Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising results due to the random assignment of initial weights and thresholds and the tendency to fall into local extreme points. In this study, a BP neural network model optimized by an improved genetic algorithm (IGA) was proposed to predict soil nutrient time series with high accuracy. First, the crossover and mutation operations of the genetic algorithm (GA) were improved. Next, the IGA was used to optimize the BP model. The symmetric nature of the model lies in its feedforward and feedback connections, i.e., the same weights must be used for the forward and backward passes. An empirical evaluation was performed using annual soil nutrient data from China. Soil pH, total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were selected as evaluation indicators. The prediction results of the IGA–BP, GA–BP, and BP neural network models were compared and analyzed. For the IGA–BP prediction model, the coefficient of determination for soil pH was 0.8, while those for total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were all greater than 0.98, exhibiting a strong generalization ability. The root-mean-square errors of the IGA–BP prediction models were reduced to 50% of the BP models. The results indicated that the IGA–BP method can accurately predict soil nutrient content for future time series.