Genetic Algorithm-back Propagation Neural Network Research Articles

Spatio-temporal evolution of social-ecological system resilience in ethnic tourism destinations in mountainous areas and trend prediction: a case study in Wuling, China

Mountainous ethnic tourism lands are important social-ecological system types. With tourism as the main disturbance factor, the theory of social-ecological system resilience provides a new way to realize the sustainable development of ethno-tourism in mountainous areas. This study divides the social-ecological system into social, economic, and ecological subsystems. It constructs an evaluation index system to assess the resilience of ethnic tourism destinations in mountainous areas, considering vulnerability and adaptability. We investigate 64 counties in the Wuling Mountain area and use set-pair analysis to assess the resilience index of the social-ecological system from 2000 to 2020 and reveal the temporal and spatial characteristics. Obstacle degree models and a genetic algorithm-back propagation neural network are utilized to determine the influencing factors and predict future development trends. The following results were obtained: (1) Temporally, the resilience index shows a steady upward trend, reaching a moderate level. The resilience of the social subsystem fluctuates and rises; the economic subsystem exhibits slow, fast, and slow growth rates with occasional abrupt changes; and the ecological subsystem demonstrates a stable, slightly increasing trend. (2) Spatially, the resilience index is high at the edges and low in the central area, exhibiting a concave distribution. Most counties have moderate or higher resilience. The social and ecological subsystems have low resilience in the south and high resilience in the north. The resilience of the economic subsystem is high at the edges and low in the central area. (3) On the distribution of major obstacle factors, the first two are similar at the county level, and the last three are significantly different. The similarity of the barrier factors is related to the degree of regional proximity of the county, and overall, the similarity is decreasing from north to south and from west to east in the distribution pattern within the area. and to a certain extent, it is affected by terrain and geomorphology. (4) The spatial distribution of the resilience index is similar in 2025 and 2030. The index decreases slightly and then increases annually, with a lower growth rate in the south than in the north. Lower values occur in the northern and southwestern parts, whereas higher values are observed around high-value areas. The region as a whole will develop in a coordinated and integrated manner in the future.

Optimal operation model of ecological flow of hydropower station based on genetic algorithm and neural network

Amid diverse water demands, the conflict between ecological flow adjustments and power generation flow adjustments in hydropower station water resource dispatching, coupled with an increasing diversity of constraints, complicates the issue of water resource optimization and dispatching. How to scientifically develop feedback regulation mechanisms to promote a virtuous ecological cycle, fine tune existing hydropower station scheduling plans, and achieve multi-objective optimization scheduling is the core issue in researching the comprehensive utilization of water resources. This study establishes a reservoir flow regulation model with the objective functions of maximizing power generation and achieving optimal average ecological protection. The model, constrained by water balance, reservoir capacity, unit output, and unit overflow, was optimized and solved using the genetic algorithm-backpropagation neural network (GA-BPNN) approach. This study establishes a reservoir flow regulation model with the GA-BPNN algorithm was applied in multi-objective optimization to identify an optimal solution that maximizes overall system performance while adhering to ecological flow constraints. The results indicate that the optimized plan has performed effectively in hydropower station operations. It not only satisfies ecological flow requirements, but also significantly enhances power generation efficiency. Specifically, power generation increased by 32,680 kw·h, a 23.85% growth rate. The optimized plan significantly enhances the comprehensive utilization efficiency of water resources. It offers a scientific basis for the rational planning, dispatching and utilization of water resources in hydropower stations and reservoirs.

Regression prediction of critical exhaust volumetric flow rate in tunnel fire with two-point extraction ventilation based on neural network method

In this study, a Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) model was developed to predict critical exhaust volumetric flow rate in tunnel fires with two-point extraction ventilation system. Seven influencing factors served as inputs for the model, while the critical exhaust volumetric flow rate was the output. Experimental data from two reduced-scale tunnels were utilized to both train and validate the GA-BPNN model. The results demonstrate a satisfactory prediction performance, with key metrics such as Mean Absolute Percentage Error (MAPE), Relation Coefficient (R2), and Root Mean Square Error (RMSE) achieving values of 0.0384, 0.9989, and 3.5258, respectively. Importantly, the model maintains a maximum Absolute Percentage Error (APE) below 10 %. In contrast, the traditional BPNN model exhibited an APE of approximately 18.9 %. Moreover, the predictive results of the GA-BPNN model were compared with those of a previously semi-empirical formula. It was observed that the semi-empirical predictions exhibited APE exceeding 20 % for certain data points. This further underscores the superiority of the GA-BPNN model in predicting critical exhaust volumetric flow rates in tunnel fires with two-point ventilation system. These findings affirm the feasibility and effectiveness of GA-BPNN regression model as a reliable method for predicting critical exhaust volumetric flow rates under multiple factors.

Research on Failure Pressure Prediction of Water Supply Pipe Based on GA-BP Neural Network

The water supply pipeline is regarded as the “lifeline” of the city. In recent years, pipeline accidents caused by aging and other factors are common and have caused large economic losses. Therefore, in order to avoid large economic losses, it is necessary to analyze the failure prediction of pipelines so that the pipelines that are going to fail can be replaced in a timely manner. In this paper, we propose a method for predicting the failure pressure of pipelines, i.e., a genetic algorithm was used to optimize the weights and thresholds of a BP neural network. The first step was to determine the topology of the neural network and the number of input and output variables. The second step was to optimize the weights and thresholds initially set for the back propagation neural network using a genetic algorithm. Finally, the optimized back-propagation neural network was used to simulate and predict pipeline failures. It was proved by examples that compared with the separate back propagation neural network model and the optimized and trained genetic algorithm-back propagation neural network, the model performed better in simulation prediction, and the prediction accuracy could reach up to 91%, whereas the unoptimized back propagation neural network model could only reach 85%. It is feasible to apply this model for fault prediction of pipelines.

Root phenotype detection of rice seedling under nitrogen conditions based on terahertz imaging technique

Root phenotype detection is the basis for screening of dominant root and improving of seed breeding. The efficiency of root phenotype detection is restricted by the concealment and complexity of crop roots. In this paper, a new method based on terahertz imaging technology was proposed to detect the phenotypic characteristics of rice root and quantitatively predict root nitrogen content. First, the terahertz imaging spectral data of rice root were preprocessed with de-overlapping, reconstruction, enhancement, and segmentation. Then, the phenotypic characteristics of root length, diameter and surface area of rice roots were extracted according to the refinement algorithm. In addition, three linear regression models were fitted between root phenotype and root nitrogen content. Finally, Convolutional Neural Network (CNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN), and Sparrow Search Algorithm-Support Vector Regression (SSA-SVR) were used to predict nitrogen content in rice roots by training and testing terahertz time domain data. Compared with two kinds of root phenotypic analysis software, the average errors of root length, diameter and root surface area calculated in this paper were 10.68 %, 7.29 % and 3.49 %, respectively. The fitting determination coefficients between root length, root surface area and the root nitrogen content were 0.88 and 0.87 after removing three groups of high-nitrogen data and one group of contrast data. The prediction accuracy of SSA-SVR model was 0.99 and the root mean square error was 0.05. Studies show that terahertz imaging technique can provide an effective analytical way for qualitative analysis of root phenotype.

Enhancing face-milling efficiency of wood–plastic composites through the application of genetic algorithm–back propagation neural network

ABSTRACT Wood–plastic composites (WPCs) have advanced physicochemical properties and are widely applied in various fields. How to achieve high-efficiency, high-quality machining of WPC is a pressing issue that WPC manufacturing enterprises need to address. To this end, this research focused primarily on improving the machinability of WPC with end milling experiments. In this work, a single-factor method was used to analyse the impact of axial milling depth, spindle speed, and tool rake angle on resultant forces and surface roughness. Main effects analysis was applied to explore the degree of influence of axial milling depth, spindle speed, and tool rake angle on cutting forces and surface roughness. Furthermore, three-dimensional characterisation techniques were utilised to analyse the surface morphology characteristics of WPC during end milling. Finally, a genetic algorithm–back propagation neural network was applied to develop prediction models for resultant force and surface roughness, and optimal milling conditions were identified as axial milling depth of 0.50 mm, spindle speed of 7841 r/min, and rake angle of 15°; these produced the lowest resultant force and surface roughness. The findings of this work are proposed as a guide for better cutting performance in the industrial production of WPC.

Prediction models for retinopathy of prematurity occurrence based on artificial neural network

IntroductionEarly prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.ObjectiveTo establish predictive models for ROP occurrence based on the risk factors using artificial neural network.MethodsA cohort of 591 infants was recruited in this retrospective study. The association between ROP and perinatal factors was analyzed by univariate analysis and multivariable logistic regression. We developed predictive models for ROP screening using back propagation neural network, which was further optimized by applying genetic algorithm method. To assess the predictive performance of the models, the areas under the curve, sensitivity, specificity, negative predictive value, positive predictive value and accuracy were used to show the performances of the prediction models.ResultsROP of any stage was found in 193 (32.7%) infants. Twelve risk factors of ROP were selected. Based on these factors, predictive models were built using BP neural network and genetic algorithm-back propagation (GA-BP) neural network. The areas under the curve for prediction models were 0.857, and 0.908 in test, respectively.ConclusionsWe developed predictive models for ROP using artificial neural network. GA-BP neural network exhibited superior predictive ability for ROP when dealing with its non-linear clinical data.

Optimization of Operation Parameters and Performance Prediction of Paddy Field Grader Based on a GA-BP Neural Network

Paddy field leveling is an essential step before rice transplanting. During the operation of a paddy field grader, a common issue is the wrapping of rice straw around the blades, resulting in a low rice straw burial rate. This study focused on analyzing the operating parameters of a disc spring–tooth-combined paddy field grader. A soil–straw mechanism simulation model was created using EDEM 2021 software to simulate the field operation status. Firstly, the single-factor test was carried out, with the working speed, the working depth of the disc cutter roller, and the rotation speed of the cutter roller as the factors and the straw-buried rate (SBR) and the machine forward resistance (MFR) as the test indexes, and the parameter range was optimized. The parameters were optimized by the response surface method (RSM) and machine learning algorithms. The results indicated that the genetic algorithm–back propagation (GA-BP) neural network outperformed other optimization models in terms of prediction accuracy and stability. By utilizing the GA-BP regression model and RSM model for regression fitting, two sets of optimal parameter combinations were obtained. Verification experiments were carried out using two sets of parameter combinations. Taking the average of the experimental results, the simulation results showed that the straw burial rate was 93.47% and the forward resistance was 6487 N for the parameter combinations of RSM, and the straw burial rate was 94.86% and the forward resistance was 6352 N for the parameter combinations of GA-BP; the field experiments showed that the straw burial rate was 92.86% and the forward resistance was 6518 N for the parameter combinations of RSM, and the straw burial rate was 95.17% and the forward resistance was 6249 N for the parameter combinations of GA-BP. The results demonstrated that the GA-BP prediction model exhibited better predictive capabilities compared to the traditional RSM, providing more accurate predictions of the paddy field grader’s field operation performance.

Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm–Backpropagation Neural Network

Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm–Backpropagation Neural Network

Optimizing the extraction process of total saponins from Panax vietnamensis based on response surface analysis and backpropagation neural network-genetic algorithm

Panax vietnamensis (PV), a member of the Araliaceae family, is noted for its saponin-rich roots and rhizomes. Recent pharmacological studies have shown its anti-inflammatory, anti-cancer, anti-myocardial ischemia, and hypoglycemic effects, establishing PV as a highly valuable variety of ginseng worldwide. Herein, we used an ultrasound-assisted method to optimize the extraction of total saponins from PV, and developed a genetic algorithm (GA) to optimize the backpropagation (BP) neural network model and predict the best process conditions. The best process with an ethanol concentration of 65.8 %, extraction times of 4.0, ultrasonic temperature of 40.7°C, and an extraction rate of 4.4103 % was achieved through response surface optimization. Conversely, the best process optimized by the GA-BP neural network model had an ethanol concentration of 70.0 %, extraction times of 4.0, ultrasonic temperature of 41.0°C, and an extraction rate of 4.5492 %. Moreover, 254 terpenoids, consisting of 182 triterpenes, 33 monoterpenes, 19 sesquiterpenes, and 20 diterpenes, along with various types of saponins, were identified through ultra-high performance liquid chromatography-MS/MS (UPLC-MS/MS). Specifically, 87 dammarane-type saponins were detected, including 46 protopanaxadiol types, 41 protopanaxatriol types, 5 octilonol types, and 18 oleanolic acid types. The total saponin extraction rate achieved through the GA-BP neural network model surpassed that of the response surface optimization process, indicating the superiority and stability of the GA-BP neural network optimized process and thus revealing the optimal method for PV total saponin extraction. The comprehensive analysis of PV saponins using UPLC-MS/MS enhanced our understanding of total saponins in PV, laying the foundations for the strategic development and utilization of PV plant resources.

Seafloor topography refinement from multisource data using genetic algorithm—backpropagation neural network

SUMMARY During the inversion of seafloor topography (ST) using the backpropagation neural network (BPNN), the random selection of parameters may decrease the accuracy. To address this issue and achieve a more efficient global search, this paper introduces a genetic algorithm-backpropagation (GA-BP) neural network. Benefiting from the global search and parallel computing capabilities of the GA, this study refines the ST of the South China Sea using multisource gravity data. The results indicate that the GA-BP model, with a root mean square (RMS) value of 126.0 m concerning ship-measured water depths. It is noteworthy that when dealing with regions characterized by sparse survey line distributions, the GA-BP neural network stronger robustness compared to BPNN, showing less sensitivity to the distribution of survey data. Furthermore, the paper explores the influence of different data pre-processing methods on the neural network inversion of sea depths. This research introduces an optimization algorithm that reduces instability during BPNN initialization, resulting in a more accurate prediction of ST.

Preparation and characterization of Ti3C2 Mxene through back propagation neural network-genetic algorithm combined with surface method response

Mxene's distinctive two-dimensional (2D) layered structure and special surface chemistry make it highly versatile for adsorption, sensors, elecatalysis, and energy storage. However, it has always been a challenge to obtain Mxene with excellent performance. In this study, the process of Ti3C2 Mxene preparation by etching Ti3AlC2 with hydrofluoric acid was optimized using the back-propagation neural network-genetic algorithm (BPNN-GA) and response surface methodology (RSM). Compared to RSM, BPNN-GA predicted better accuracy for preparation conditions with higher values for R2 (0.9830), MSE (2.6188), RMSE (1.6183), MAE (0.8504), and MAPE (0.0089 %). The BPNN-GA optimized etching values for HF concentration, time, and temperature were 8.81 %, 7.01 h, and 45.07 ℃, respectively. Under these conditions, the Ti3C2 Mxene prepared achieved 99.39 % removal efficiency of methylene blue (MB). The preparation was validated by XRD, SEM, TEM, XPS, Zeta, and FTIR analyses, indicating Ti3C2 Mxene to be a two-dimensional crystal structure with a multilayer arrangement and surface termination groups. Zeta measurements indicated that its surface carried negative charges across a wide pH range and its suitability for cationic dye adsorption. Using BPNN-GA provides valuable guidance and a novel strategy for material preparation.

Alignment Prediction of Air Spring Vibration Isolation System for Marine Shafting Based on Genetic Algorithm-Back Propagation Neural Network.

Shafting alignment plays an important role in the marine propulsion system, which affects the safety and stability of ship operation. Air spring vibration isolation systems (ASVISs) for marine shafting can not only reduce mechanical noise but also help control alignment state by actively adjusting air spring pressures. Alignment prediction is the first and a key step in the alignment control of ASVISs. However, in large-scale ASVISs, due to factors such as strong interference and raft deformation, alignment prediction faces problems such as alignment measurement sensors failure and difficulty in establishing a mathematical model. To address this problem, a data model for predicting alignment state is developed based on a back propagation (BP) neural network, fully taking advantage of its self-learning and self-adaption abilities. The proposed model exploits the collected data in the ASVIS instead of the alignment measurement data to calculate the alignment state, providing another alignment prediction approach. Then, in order to solve the local optimum issue of BP neural network, we introduce the genetic algorithm (GA) to optimize the weights and thresholds of the BP neural network, and an improved GA-BP model is designed. The GA-BP model can leverage the advantages of the global search capability of GA as well as the BP neural network's fast convergence in local search. Finally, we conduct experiments on a real ASVIS and evaluate the prediction models using different criteria. The experimental results show that the proposed prediction model with the GA-BP neural network can accurately predict the alignment state, with a mean-square error (MSE) of 0.0114. And compared to the BP neural network, the GA-BP neural network reduces the MSE by approximately 74%.

Wearable glove gesture recognition based on fiber Bragg grating sensing using genetic algorithm-back propagation neural network

The recognition of gestures through intelligent wearable devices has a broad array of applications, including low-visibility industrial sites and robotics. However, accurately and swiftly recognizing gestures poses a significant challenge. This study addresses the complex interplay between multimodal information sensing and signal processing with flexible sensors in wearable data gloves by conducting theoretical and experimental investigations into gesture recognition using fiber Bragg grating (FBG) flexible sensors. First, the degree of bending and bending angle of the FBG sensor were calibrated, and the impact of temperature was investigated. Subsequently, a human hand wearable data glove gesture recognition system based on fiber grating sensing was constructed, and gesture recognition experiments were conducted. Finally, an enhanced error backpropagation (BP) neural network model for gesture recognition based on genetic algorithms (GA) was established and tested and the results were compared with those achieved using a conventional error BP neural network. The comparison revealed that under the same network structure, the recognition accuracy of the GA-BP neural network of approximately 100% was higher than the conventional BP neural network’s accuracy of 95.77%. Thus, the development of gesture recognition using FBG sensors provides a theoretical foundation and technical support for multiple applications of wearable data gloves, driving their adoption in areas such as human–computer interactions, intelligent robotics, and complex high-risk environments.

Integrated measurement of public safety risks in international construction projects in the belt and road initiative

PurposeThe purpose of this paper is to develop a comprehensive understanding of the public safety risks of international construction projects (ICPs) from the perspective of threat and vulnerability. A novel and comprehensive risk assessment approach is developed from a systemic perspective and applied to the Belt and Road Initiative (BRI) to improve the public safety risk management strategy for ICPs in BRI.Design/methodology/approachFirst, a public safety risk indicator system was constructed from the two dimensions, namely threat and vulnerability. Next, an integrated measurement model was constructed by combining the Genetic Algorithm-Backpropagation (GA-BP) neural network, fuzzy comprehensive evaluation method and matter-element extension (MME) method. Data from 49 countries involved in the BRI, as well as five typical projects, were used to validate the model. Finally, targeted risk prevention measures were identified for use at the national, enterprise and project levels.FindingsThe findings indicate that while the vulnerability risks of typical projects in each region of the BRI were generally low, threat risks were high in West Asia and North Africa, Commonwealth of Independent States (CIS) countries and South Asia.Originality/valueFirst, the structure of the public safety risk system of ICPs was analyzed using vulnerability and system theories. The connotation of public safety risk was defined based on two dimensions, namely threat and vulnerability. The idea of measuring threat risk with public data and measuring vulnerability risk with project data was clarified, and the risk measurement was integrated into the measurement results to help researchers and managers understand and systematically consider the public safety risks of ICPs. Second, a public safety risk indicator system was constructed, including 18 threat risk indicators and 14 vulnerability risk indicators to address the gaps in the existing research. The MEE model was employed to overcome the problem of incompatible indicator systems and provide stable and credible integrated measurement results. Finally, the whole-process public safety risk management scheme designed in this study can help to both provide a reference point for the Chinese enterprises and oversea contractors in market selection as well as improve ICP public safety risk management.

Conditions optimization and mechanistic study of chlortetracycline hydrochloride adsorption by TBAOH-MXene: Prediction results based on backpropagation neural network-genetic algorithm and response surface methodology

MXene is extensively utilized in environmental remediation owing to its attributes of hydrophobicity and high specific surface area. Herein, backpropagation neural network-genetic algorithm (BPNN-GA) and response surface methodology (RSM) were utilized to optimize the adsorption conditions of chlortetracycline hydrochloride (CTC) employing tetrabutylammonium hydroxide intercalated niobium-based MXene (TBAOH-MXene). Compared to RSM, BPNN-GA showed higher accuracy in predicting the adsorption conditions with R2 (0.9923), χ2 (0.2300), MAE (0.3162), MSE (0.6432), MAPE (0.0038 %), and RMSE (0.8020). The maximum removal efficiency of CTC of 89.25 % occurred at an adsorbent dose of 20 mg, initial concentration of 10 mg/L, contact time of 90 min, and pH of 7 by the BPNN-GA optimization method. Based on this condition, the adsorption of CTC by TBAOH-MXene followed a pseudo-second-order and Freundlich mode, and the adsorption occurred spontaneously and endothermally. After five cycles, the removal efficiency of CTC was still as high as 75.31 %, indicating good cyclic stability of TBAOH-MXene. In conclusion, the use of BPNN-GA provides valuable guidance and introduces a new strategy for the optimization of adsorption conditions and further mechanistic investigation.

Performance prediction and operating parameters optimization for proton exchange membrane fuel cell based on data-driven surrogate model and particle swarm optimization

The operating parameters of proton exchange membrane fuel cell (PEMFC) are critical to its performance and working life. This study presents a data-driven modeling approach combining a surrogate model with the particle swarm optimization algorithm to optimize operating parameters for PEMFC and obtain the maximum power density. The results show that the operating parameters significantly influence power density under high current densities, with inlet temperature having the most significant effect. Lower inlet temperature, relative humidity in the cathode and anode, along with higher operating pressure yield improved output performance. The Genetic Algorithm-Backpropagation Neural Network based surrogate model exhibits excellent predictive performance with correlation coefficients of 0.99896 and 0.99815 for the training and test sets, respectively. Optimized conditions achieve a 3.3% increase in power density compared to initial settings, with only a 0.15% error in simulation calculations. This data-driven approach provides valuable insights for maximizing PEMFC efficiency and performance.

Enhancing urban blue-green landscape quality assessment through hybrid genetic algorithm-back propagation (GA-BP) neural network approach: a case study in Fucheng, China

This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network’s approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.

Multi-uncertainty river health assessment in the Loess Plateau based on a novel comprehensive similarity cloud model

With the development of related research on river health assessment, the traditional assessment methods cannot solve the uncertainty problems caused by diversified indicators and ambiguous weights. In order to comprehensively understand the river health status in the Loess Plateau and solve the uncertainty of assessment results, this study takes Wuding River as the research object, and constructs an assessment indicator system incorporating the ecological environment and social functions of the river. The genetic algorithm-BP neural network was used to establish a weight learning mechanism for assessment indicators, and the system weight was calculated by combining various weighting preferences. Based on the cloud model theory, a sample data cloud and a health level cloud were constructed, and a comprehensive similarity model considering the shape and distance of the cloud diagram was developed to identify the health level of Wuding River. The spatial and temporal variability of the river health status and the sources of pressure causing unhealthy problems were analyzed. It is shown that Wuding River is in basic health status, which gradually decreases from the upstream reaches to the downstream reaches. The health status of social service functions is better than that of the ecological environment attributes such as water resources and water quality. The sources of health pressure in the upstream and downstream reaches are different. In the upstream reaches, the pressure mainly comes from natural attributes such as successive declines in water production, slow governance of water and soil erosion, and the poor stability of the river. The health pressure in the midstream and dowmstream reaches comes from the interference of human economic activities, which include deterioration of water quality caused by the discharge of fossil energy industry, destruction of aquatic habitats, and flood safety hazards caused by insufficient standards for flood control project design. In addition, SDSCM provides river managers with accurate and comprehensive health risk information to help them manage rivers more effectively.

Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment

The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribution law of the bulk grain pile porosity in grain silos, machine learning algorithms were incorporated into the prediction model for grain porosity. Firstly, this study acquired the database by conducting compression experiments on grain specimens and collecting data from the literature. The back propagation neural network (BPNN) algorithm was optimized using three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA)). Five machine learning models (GA–BPNN, PSO–BPNN, WOA–BPNN, BPNN, and random forest (RF)) were developed to predict the grain porosity using three input parameters (vertical pressure, grain type, and moisture content). The five models were assessed using four evaluation metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to determine the best porosity prediction model. Finally, the generalization ability of the best prediction model was verified using the results of the grain cell box experiment on wheat piles. The results indicated that the WOA–BPNN model was the best prediction model with an R2 value of 0.9542, an RMSE value of 0.0079, an MAE value of 0.0044, and an MAPE value of 1.1467%. The WOA–BPNN model demonstrated strong generalization ability, confirming the feasibility of using this model to predict grain porosity. It also established an expression for the relationship between wheat porosity and the vertical pressure of the grain pile. This study presents a machine learning prediction method for determining the porosity of grain piles. The obtained porosity distribution law serves as a crucial basis for conducting comprehensive multi-field coupling analysis of grain piles and offers theoretical support for safe grain storage.