A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection

Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

Artificial intelligence optimization and controllable slow-release iron sulfide realizes efficient separation of copper and arsenic in strongly acidic wastewater

Iron sulfide (FeS) is a promising material for separating copper and arsenic from strongly acidic wastewater due to its S2- slow-release effect. However, uncertainties arise because of the constant changes in wastewater composition, affecting the selection of operating parameters and FeS types. In this study, the aging method was first used to prepare various controllable FeS nanoparticles to weaken the arsenic removal ability without affecting the copper removal. Orthogonal experiments were conducted, and the results identified the Cu/As ratio, H2SO4 concentration, and FeS dosage as the three main factors influencing the separation efficiency. The backpropagation artificial neural network (BP-ANN) model was established to determine the relationship between the influencing factors and the separation efficiency. The correlation coefficient (R) of overall model was 0.9923 after optimizing using genetic algorithm (GA). The BP-GA model was also solved using GA under specific constraints, predicting the best solution for the separation process in real-time. The predicted results show that the high temperature and long aging time of FeS were necessary to gain high separation efficiency, and the maximum separation factor can reached 1,400. This study provides a suitable sulfurizing material and a set of methods and models with robust flexibility that can successfully predict the separation efficiency of copper and arsenic from highly acidic environments.

Comparison, validation and improvement of empirical soil moisture models for conditions in Colombia

Modeling soil moisture as a function of meteorological data is necessary for agricultural applications, including irrigation scheduling. In this study, empirical water balance models and empirical compartment models are assessed for estimating soil moisture, for three locations in Colombia. The daily precipitation and average, maximum and minimum air temperatures are the input variables. In the water balance type models, the evapotranspiration term is based on the Hargreaves model, whereas the runoff and percolation terms are functions of precipitation and soil moisture. The models are calibrated using field data from each location. The main contributions compared to closely related studies are: i) the proposal of three models, formulated by combining an empirical water balance model with modifications in the precipitation, runoff, percolation and evapotranspiration terms, using functions recently proposed in the current literature and incorporating new modifications to these terms; ii) the assessment of the effect of model parameters on the fitting quality and determination of the parameters with higher effects; iii) the comparison of the proposed empirical models with recent empirical models from the literature in terms of the combination of fitting accuracy and number of parameters through the Akaike Information Criterion (AIC), and also the Nash-Sutcliffe (NS) coefficient and the root mean square error. The best models described soil moisture with an NS efficiency higher than 0.8. No single model achieved the highest performance for the three locations.

Quality Grading of River Crabs Based on Machine Vision and GA-BPNN

The prices of different quality river crabs on the market can vary several times. Therefore, the internal quality identification and accurate sorting of crabs are particularly important for improving the economic benefits of the industry. Using existing sorting methods by labor and weight to meet the urgent needs of mechanization and intelligence in the crab breeding industry is difficult. Therefore, this paper proposes an improved BP neural network model based on a genetic algorithm, which can grade the crab quality. We comprehensively considered the four characteristics of crabs as the input variables of the model, namely gender, fatness, weight, and shell color of crabs, among which gender, fatness, and shell color were obtained by image processing technology, whereas weight is obtained using a load cell. First, mature machine vision technology is used to preprocess the images of the crab's abdomen and back, and then feature information is extracted from the images. Next, genetic and backpropagation algorithms are combined to establish a quality grading model for crab, and data training is conducted on the model to obtain the optimal threshold and weight values. Analysis of experimental results reveals that the average classification accuracy reaches 92.7%, which proves that this method can achieve efficient and accurate classification and sorting of crabs, successfully addressing market demand.

Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation

Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R²) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R² of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R² of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R² of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.

A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland

Soil moisture plays an important role in ecology, hydrology, agriculture and climate change. This study proposes a soil moisture prediction model, based on the depth and water balance equation, which integrates the water balance equation with the seasonal ARIMA model, and introduces the depth parameter to consider the soil moisture at different depths. The experimental results showed that the model proposed in this study was able to provide a higher prediction accuracy for the soil moisture at 40 cm, 100 cm and 200 cm depths, compared to the seasonal ARIMA model. Different models were used for different depths. In this study, the seasonal ARIMA model was used at 10 cm, and the proposed model was used at 40 cm, 100 cm and 200 cm, from which more accurate prediction values could be obtained. The fluctuation of the predicted data has a certain seasonal trend, but the regularity decreases with the increasing depth until the soil moisture is almost independent of the external influence at a 200 cm depth. The accurate prediction of the soil moisture can contribute to the scientific management of the grasslands, thus promoting ecological stability and the sustainable development of the grasslands while rationalizing land use.

Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network

As the mobile Internet improves by leaps and bounds, the model of traditional offline used car trading has gradually lost the ability to live up to the needs of consumers, and online used car trading platforms have emerged as the times require. Second-hand car price assessment is the premise of second-hand car trading, and a reasonable price can reflect the objective, fair, and true nature of the second-hand car market. In order to standardize the evaluation standards of used car prices and improve the accuracy of used car price forecasts, the linear correlation between vehicle parameters, vehicle conditions, and transaction factors and used car price was comprehensively investigated, grey relational analysis was applied to filter the feature variables of factors affecting used car price, the traditional BP neural network was also optimized by combining the particle swarm optimization algorithm, and a used car price prediction method based on PSO-GRA-BPNN was proposed. The results show that only the correlation coefficient of new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation. The correlation between new car price, displacement, mileage, gearbox type, fuel consumption, and registration time on used car prices is greater than 0.7, and the impact of other indicators on used car prices is negligible. Compared with the traditional BPNN model and the multiple linear regression, random forest, and support vector machine regression models proposed by other researchers, the MAPE of the PSO-GRA-BPNN model proposed in this paper is 3.936%, which is 30.041% smaller than the error of the other three models. The MAE of the PSO-GRA-BPNN model is 0.475, which is a maximum reduction of 0.622 compared to the other three models. R can reach up to 0.998, and R2 can reach 0.984. Although the longest training time is 94.153 s, the overall prediction effect is significantly better than other used car price prediction models, providing a new idea and method for used car evaluation.

Application of Machine Learning and Remote Sensing in Hydrology

Water is vital to all life on earth, but its management is becoming more difficult owing to the behavior of water in nature such as water dynamics, water movements, and different forms of water in nature [...]

Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning

Maintaining the stability of highway soft rock slopes is of critical importance for ensuring the safety of road networks. Although much research has been carried out to assess the stability of individual soft rock slope, the goal of efficient and effective risk management focusing on multiple highway soft rock slopes has not been fully achieved due to the many complex factors involved and the interactions among these factors. In the present study, a machine learning algorithm based on a fuzzy neural network (FNN) and a comprehensive evaluation method based on the FNN is developed, in order to identify and issue early warnings regarding the risks induced by soft rock slopes along highways, in an efficient and effective way. Using a large amount of collected soft rock slope information as training and validation data, an FNN-based risk identification model is first developed to identify the risk level of individual soft rock slope based on the meteorological conditions, topographical and geomorphological factors, geotechnical properties, and the measured horizontal displacement. An FNN-based comprehensive evaluation method is then developed, in order to quantify the risk level of a soft rock slope group according to the slope, road and external factors. The results show that the risk level identification accuracy obtained based on validation of the FNN model was higher than 90%, and the model showed a good training effect. On this basis, we further made early warnings of the risks of soft rock slope groups. The proposed early-warning model can quickly and accurately evaluate the risk posed by multiple soft rock slopes to a highway, thereby ensuring the safety of the highway.