Relative Deviation Values Research Articles

Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion

For efficient and comprehensive detection of the staling degree of Chinese steamed bread (CSB), staled CSB samples stored for 0–16 days were prepared and analyzed using near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy combined with data fusion. Among three data fusion schemes, decision-level fusion achieved the best performance when quantifying the CSB staling degree according to the soluble starch amylose fraction, relative crystallinity, and hardness, with determination coefficients and root mean square errors for the validation set in the range of 0.928–0.986 and 0.015–1.290, respectively. The relative percent deviation values of the three indicators increased to 8.362, 4.735, and 3.617, respectively. These results indicate that the combination of NIR, MIR, and Raman spectroscopy as a decision-level fusion scheme can achieve efficient, comprehensive, and accurate quantification of the staling degree of CSB. This research has important applications for food quality, safety, and shelf-life evaluations.

Artificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries.

This study aimed to optimize the postharvest blanching and drying process of blueberries using high-humidity air impingement (HHAIB) and hot-air-assisted infrared (HAIR) heating. A novel pilot-scale hot-air-assisted carbon-fiber infrared (IR) blanching/drying system was developed. Fresh blueberries with an average diameter of 10~15 mm were first blanched with high-humidity air at 110 °C and 12 m/s velocity for different durations (30, 60, 90, and 120 s); subsequently, the preblanched blueberries were dried at different IR heating temperatures (50, 60, 70, 80, and 90 °C) and air velocities (0.01, 0.5, 1.5, and 2.5 m/s), following a factorial design. The drying time (DT), specific energy consumption (SEC), ascorbic acid content (VC), and rehydration capacity (RC) were determined as response variables. A three-layer feed-forward artificial neural network (ANN) model with a backpropagation algorithm was constructed to simulate the influence of blanching time, IR heating temperature, and air velocity on the four response variables by training on the experimental data. Objective functions for DT, SEC, VC, and RC that were developed by the ANN model were used for the simultaneous minimization of DT and SEC and maximization of VC and RC using a nondominated sorting genetic algorithm (NSGA II) to find the Pareto-optimal solutions. The optimal conditions were found to be 93 s of blanching, 89 °C IR heating, and a 1.2 m/s air velocity, which resulted in a drying time of 366.7 min, an SEC of 1.43 MJ/kg, a VC of 4.19 mg/100g, and an RC of 3.35. The predicted values from the ANN model agreed well with the experimental data under optimized conditions, with a low relative deviation value of 1.43-3.08%. The findings from this study provide guidance to improve the processing efficiency, product quality, and sustainability of blueberry postharvest processes. The ANN-assisted optimization approach developed in this study also sets a foundation for the smart control of processing systems of blueberries and similar commodities.

Sugarcane Yield Estimation of Yamunanagar and Panipat District of Haryana Using NARX Model

Uncontrollable factors that affect yield variability include seasonal and intra seasonal weather variations. The degree to which meteorological factors affect agricultural productivity depends not only on their quantity but also on how they are distributed throughout the crop season. Therefore, while estimating the dynamic behavior of agricultural production, it should be able to take advantage from both historical data on crop yield and the impact of different external environmental driving forces. In this study, Yamunanagar and Panipat districts of Haryana's sugarcane yield have been attempted to be estimated using NARX (Nonlinear Autoregressive Model with Exogenous Input) models. The NARX model with one hidden neuron and arf13, and the NARX model with two hidden neuron and tmx13 selected as best model for Yamunanagar and Panipat districts respectively with smaller values of percent relative deviation (RD (%)), mean absolute percentage error (MAPE) and root mean squared error (RMSE).

Crystallization thermodynamics of 2,4(5)-dinitroimidazole in binary solvents

2,4(5)-Dinitroimidazole (2,4(5)-DNI) is an important organic intermediate, and itself can also be used for energetic material. In this work, the solubility of 2,4(5)-DNI in (methanol + water, acetonitrile + water, acetone + water) binary solvents were measured by using a dynamic test method from 278.15 K to 323.15 K under 101.1 kPa. The Jouyban–Acree model, van’t Hoff–Jouyban–Acree model, Apelblat–Jouyban–Acree model, Ma model, and Sun model were used to correlate the experimental data. The values of relative average deviation (RAD) and root-mean-square deviation (RMSD) were very small, indicating that the error between the experimental value and the correlated value was very small. The thermodynamic parameters such as dissolution enthalpy, dissolution entropy and Gibbs energy were calculated based on solubility data. High-purity of 2,4(5)-DNI was efficiently obtained by using cooling and dilution crystallization method.

The dissolution behavior and thermodynamic properties analysis of pentoxyverine citrate in aqueous solutions of alcohols

The objective of this work is to study the dissolution behavior and thermodynamic properties of pentoxyverine citrate in aqueous solutions of alcohols. The results of solubility data at temperature range from 278.15 K to 318.15 K were obtained by the isothermal equilibrium method under atmospheric condition. In pure solvents, the largest solubility data in mole fraction is found in water at 318.15 K (2.756 × 10−2), followed by ethanol (1.156 × 10−2), n-propanol (8.129 × 10−3), isobutanol (5.233 × 10−3), isopropanol (4.340 × 10−3) and 1-butanol (3.435 × 10−3). The order of solubility from large to small is consistent with the order of solvent polarity. The solubility profile in mixtures of (ethanol + water), (n-propanol + water) and (isopropanol + water) increases firstly and then decreases with the increasing mass fraction of alcohols at all studied temperatures, and it reaches a maximum at w = 0.60 (the mass fraction of ethanol) in mixtures of (ethanol + water) at all investigated temperatures, for two other binary mixtures, the mass fraction of n-propanol and isopropanol is 0.40. The analysis of extended Hildebrand solubility approach indicate the solvent–solvent interactions and specific interactions are unfavorable for the dissolution process. The values of relative average deviation (RAD) for crystalline pentoverine citrate demonstrated well correlation of solubility values with Apelblat equation and Jouyban-Acree model. The positive values of ΔHsolo and ΔSsolo for dissolution behavior indicate the dissolution process was not only endothermic but also entropic-favouring. Moreover, the enthalpy is the main contributing force to the Gibbs free energy during the dissolution of pentoverine citrate.

Estimation of Critical and Thermophysical Properties of Saturated Cyclic Alkanes by Group Contributions

Group Contribution Methods (GCMs) of Marrero & Gani (M & G), Constantinou & Gani (C & G) and Lydersen (LYD) were employed in the prediction of some critical and thermophysical properties(TPPs), namely; critical temperature (Tc), critical pressure (Pc), critical volume (Vc), boiling temperature (Tb) and melting temperature (Tm), for various cycloalkanes. The predicted properties were compared with available experimental data. Experimental data for T m were unfortunately very scanty in the open literature; no comparison was done to appraise any of the methods. For Tc , LYD, C & G and M & G gave average relative deviation (ARD) values of 0.02%, 1.57% and 14.64%, respectively. In the case of Vc , ARD values for LYD, C & G and M & G are 2.6 %, 23.97 %, -4.53 %, respectively. Predicted P c values using the methods gave ARD of -31.55 %, -1.49 %, -277.98 %, respectively. C & G and M & G recorded ARD values of 0.63 % and 10.30 % for Tb , respectively.

Influence of high pressure on the solubility of ribavirin in six pure solvents from 283.15 to 323.15 K

The solubility of ribavirin in six pure solvents was measured at 9.0 MPa and 0.1 MPa to evaluate the influence of high pressure on solubility at the temperature range from 283.15 K to 323.15 K. The results indicated that all the solubilities of ribavirin decreased slightly at 9.0 MPa compared with those at 0.1 MPa. Solvation free energy of ribavirin in six solvents under two pressures was calculated to explain the decrease of solubility under high pressure. Besides, the solubility of ribavirin increased with the rising temperature under both 0.1 MPa and 9.0 MPa. Furthermore, the experimental solubility data under two pressures were correlated by the modified Apelblat equation and NRTL model. The average relative deviation (ARD%) value of the modified Apelblat equation is less than 3 %, which can give better correlation results. Finally, the thermodynamic properties of mixing and dissolution of ribavirin in six pure solvents under two pressures were also calculated based on the solubility experimental data and the NRTL equation.

Caffeine solubility in aqueous solutions of polyethylene glycol dimethyl ether 250: measurement and thermodynamic modeling

ABSTRACT Molar solubility of caffeine in polyethylene glycol dimethyl ether 250 (PEGDME 250) + water mixtures over all mass fractions of PEGDME 250 were determined at 293.15–313.15 K under atmospheric pressure (≈ 85 kPa). The crystal forms of the caffeine in equilibrium with neat solvents of PEGDME 250 and water were analysed using X-ray powder diffraction and the results compared with the original caffeine. Also, the Hansen solubility parameters of caffeine and PEGDME 250 + water mixtures in the absence of caffeine were predicted. The studies of apparent thermodynamic analysis presented an endothermic and enthalpy-driven dissolution of caffeine. Moreover, the solubility of caffeine were correlated using various models; the van’t Hoff, mixture response surface, Jouyban–Acree, Jouyban–Acree–van’t Hoff, the modified version of Jouyban–Acree–van’t Hoff, the CNIBS/R-K, the Buchowski–Ksiazczak, the modified Wilson and modified Wilson–van’t Hoff models, acquiring average relative deviation values that were lower than 14.7%.

Study of solute-solvent intermolecular interactions and preferential solvation for mevastatin dissolution in pure and mixed binary solvents

The solute-solvent and solvent-solvent interactions between mevastatin and different solvents were studied. In pure solvent, the largest solubility was found in 2-butanone (2.592×10−2) and minimum in cyclohexane (5.068×10−5) at 318.15 K. The non-specific and hydrogen bonding interactions including the dipolarity/polarizability (π*), Hildebrand solubility parameter (δH), hydrogen bond acidity (α) and hydrogen bond basicity (β) were analyzed. In mixture of (ethyl acetate + ethanol), the maximum solubility (2.144×10−2) is observed in 80%: 20% w/w of ethyl acetate: ethanol. Almost the same interaction energy and equilibrium mixing energy between solute and solvent promote the occurrence of co-solvency phenomenon. Moreover, according to the preferential solvation results, it is conjecturable that in intermediate composition mixtures and ethyl acetate-rich mixtures, mevastatin molecules are acting mainly as Lewis acid and Lewis base, respectively. Meanwhile, two thermodynamic models including modified Apelblat equation and Jouyban-Acree model are suitable to evaluate the dissolution profile, and the maximum value of relative average deviation is no >5%. The apparent thermodynamic properties of dissolution process in all investigated solvents are calculated which suggest that dissolution process is an endothermic process.

Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China

Soil organic matter (SOM) is a key index of soil fertility. Visible and near-infrared (VNIR, 350–2500 nm) reflectance spectroscopy is an effective method for modeling SOM content. Characteristic wavelength screening and spectral transformation may improve the performance of SOM prediction. This study aimed to explore the optimal combination of characteristic wavelength selection and spectral transformation for hyperspectral modeling of SOM. A total of 219 topsoil (0–20 cm) samples were collected from two soil types in the East China. VNIR reflectance spectra were measured in the laboratory. Firstly, after spectral transformation (inverse-log reflectance (LR), continuum removal (CR) and first-order derivative reflectance (FDR)) of VNIR spectra, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variables elimination (UVE) algorithms. Secondly, the SOM prediction models were constructed based on the partial least squares regression (PLSR), random forest (RF) and support vector regression (SVR) methods using the full spectra and selected wavelengths, respectively. Finally, optimal SOM prediction models were selected for two soil types. The results were as follows: (1) The CARS algorithm screened 40–125 characteristic wavelengths from the full spectra. The UVE algorithm screened 105–884 characteristic wavelengths. (2) For two soil types and full spectra, CARS and UVE improved the SOM modeling precision based on the PLSR and SVR methods. The coefficient of determination (R2) value in the validation of the CARS-PLSR (PLSR model combined with CARS) and CARS-SVR (SVR model combined CARS) models ranged from 0.69 to 0.95, and the relative percent deviation (RPD) value ranged from 1.74 to 4.31. Lin’s concordance correlation coefficient (LCCC) values ranged from 0.83 to 0.97. The UVE-PLSR and UVE-SVR models showed moderate precision. (3) The PLSR and SVR modeling accuracies of Paddy soil were better than those for Shajiang black soil. RF models performed worse for both soil types, with the R2 values of validation ranging from 0.22 to 0.68 and RPD values ranging from 1.01 to 1.60. (4) For Paddy soil, the optimal SOM prediction models (highest R2 and RPD, lowest root mean square error (RMSE)) were CR-CARS-PLSR (R2 and RMSE: 0.97 and 1.21 g/kg in calibration sets, 0.95 and 1.72 g/kg in validation sets, RPD: 4.31) and CR-CARS-SVR (R2 and RMSE: 0.98 and 1.04 g/kg in calibration sets, 0.91 and 2.24 g/kg in validation sets, RPD: 3.37). For Shajiang black soil, the optimal SOM prediction models were LR-CARS-PLSR (R2 and RMSE: 0.95 and 0.93 g/kg in calibration sets, 0.86 and 1.44 g/kg in validation sets, RPD: 2.62) and FDR-CARS-SVR (R2 and RMSE: 0.99 and 0.45 g/kg in calibration sets, 0.83 and 1.58 g/kg in validation sets, RPD: 2.38). The results suggested that the CARS algorithm combined CR and FDR can significantly improve the modeling accuracy of SOM content.

Construction and Evaluation of Prediction Model of Main Soil Nutrients Based on Spectral Information

The rapid and accurate detection of soil nutrient content through spectral technology is one of the requisite technologies for precision fertilization, which, however, is an unsolved issue. In order to achieve this purpose, a more robust and accurate model is established in this study. The regression algorithm is integrated with effective wavelength selection to construct the prediction model for total nitrogen, available phosphorus, and available potassium (N, P, and K), which removes the need for complex pretreatment and algorithm constraints. According to the research results, with regard to the prediction of soil nitrogen, phosphorus, and potassium contents, the joint interval support vector regression (Si-SVR) model performed best in modeling, with the root mean square error of prediction (RMSEP) limited to 0.0231%, 1.0554%, and 3.4225%, respectively. In addition, the relative percentage deviation (RPD) values were restricted to 2.68, 2.12, and 2.37, respectively. As indicated by the prediction results obtained for the above three nutrient contents, the RPD values of the Si-SVR model prediction accuracy evaluation indicators exceeded 2.0, which evidences a high level of prediction accuracy. This method makes it possible for spectral data to be applied in practical production, and these results provide a valuable reference for the effective detection of major soil nutrients.

Nondestructive detection of tomato quality based on multiregion combination model

AbstractTo quickly and accurately identify the quality of tomatoes, a method was proposed to predict the total soluble solid content (SSC), total titratable acidity (TA), and vitamin C (VC) content of tomatoes based on a multiregion combined model of the visible–near‐infrared spectrum. The results show that the competitive adaptive re‐weighted sampling algorithm combined with the partial least squares regression (CARS‐PLSR) model has the best prediction effect on SSC, TA, and VC content in “stem + equator”, “stem + bottom” and “stem + bottom” combinations. The prediction accuracy is 97.2%, 96.7%, and 97.7%, respectively, and the relative percent deviation (RPD) value is 5.870, 5.401, and 5.942, respectively.Practical ApplicationThis indicates that the CARS‐PLSR model based on the multiregion combination of visible–near‐infrared spectroscopy is reliable for predicting tomatoes' SSC, TA, and VC content. The results provide a theoretical basis for developing a portable fruit quality detector.

Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine

Dried roots of Polygala tenuifolia (YuanZhi in Chinese) are widely used in Chinese herbal medicine. These components in YuanZhi have significant anti-oxidation properties owing to high levels of 3,6’-disinapoylsucrose (DISS) and Polygalaxanthone III (PolyIII). In order to efficiently extract natural medicines, response surface methodology (RSM) and least squares support vector machine (LSSVM) were used for the modeling and optimization of ultrasound-assisted extraction of DISS and PolyIII together to determine the antioxidant activity of the extracts obtained from YuanZhi. For the optimal combination of the comprehensive yield of DISS and PolyIII (Y), the Box-Behnken design (BBD) was used to improve extraction time (X1), extraction temperature (X2), liquid–solid ratio (X3), and ethanol concentration (X4). The optimal process parameters were determined to be as follows: extraction time, 93 min; liquid–solid ratio, 40 mL/g; extraction temperature, 48 °C; and ethanol concentration, 67%. With these conditions, the predictive optimal combination comprehensive evaluation value is 13.0217. It was clear that the LS-SVM model had higher accuracy in predictive and optimization capabilities, with higher antioxidant activity and lower relative deviations values, than did RSM. Hence, the LS-SVM model proved to be more effective for the analysis and improvement of the extraction process.

Validation of Precipitation Events in ERA5 to Gauge Observations during Warm Seasons over Eastern China

AbstractPrecipitation events should be characterized using data with high temporal resolution, such as hourly precipitation. Event-based evaluation can provide more information than the traditional equal-time-interval method by considering precipitation intermittency. This study focuses on the performance of hourly gauge observations and ERA5 products based on precipitation events in eastern China during 1979–2015. The annual frequency, duration, amount, and intensity of precipitation events are compared, and the statistics of precipitation events with different durations are also evaluated. Results show that ERA5 estimated more annual precipitation events and longer duration compared to the gauge observations, with relative deviation values of 48.75% and 49.22% at the national scale. Precipitation intensity and amount estimated by ERA5 based on precipitation events were less than those obtained from gauge observations, and the discrepancies in low-latitude regions were greater than those in high-latitude areas. The frequency of precipitation events decreased exponentially with duration for both ERA5 and gauge observations, but generally the value for the former was larger than for the latter. The statistics related to precipitation events showed smaller trends for ERA5 than for gauge observations, i.e., −0.13 h decade−1and −0.17 mm decade−1for the trends of duration and amount in ERA5, which contrasts with 0.03 h decade−1and 0.14 mm decade−1for gauge observations, respectively. These results can provide a reference for improving the parameterization scheme of the precipitation triggering mechanism in the process of model simulation.

The air distribution and flow resistance of tuyeres with coupling effect

The tuyere plays a critical role in creating a comfortable the indoor environment and is significant for the obtaining hydraulic balance in ventilation and air condition (VAC) systems. Currently, the internal flow state of the tuyeres in VAC duct engineering design is idealized for full development. Ignoring the coupling effect causes the problem of ineffective air distribution and places an enormous load on the entire system. Therefore, the deviation in the air distribution and the change in the flow resistance of circular diffusers and spherical nozzles under the coupling effect are studied through full-scale experiments and numerical simulations. In this research, the deviation law under coupling effect conditions is presented, and the relative deviation formula of the circular diffuser under the coupling effect is fitted. Considering the total pressure loss, relative deviation, and target value, a table that shows connection approaches in engineering applicable to circular diffusers is proposed. The reduced structure of the spherical nozzle overcomes the disturbance caused by the coupling effect but increases the resistance by approximately 50.8%. Consequently, the research results from the study of tuyeres under the coupling effect are expected to lead to a reduction in unnecessary energy consumption and supplement the traditional design theory of the VAC duct system.

A collaborative iterative greedy algorithm for the scheduling of distributed heterogeneous hybrid flow shop with blocking constraints

The hybrid flow shop and distributed flow shop problems have been extensively studied due to their wide industrial applications. However, the distributed heterogeneous hybrid flow shop problems (DHHFSP) with blocking constraints have not yet been well studied up to date. This paper considers how to arrange a variety of jobs to different heterogeneous factories, and each factory has a minimal makespan. The innovations of this paper lie in presenting a mathematical model of the DHHFSP with blocking constraints and designing a collaborative iterative greedy (CIG) algorithm. The CIG contains the problem-specific initialization strategy, the neighborhood search strategy, the destruction-reconstruction strategy, and the local intensification strategy. The cross-factory and inner-factory neighborhood search strategies based on two swap operators are adopted to reduce the blocking time. The local intensification strategy is developed to optimize the scheduling sequence of each factory. The proposed algorithm is empirically compared with five state-of-the-art algorithms on 60 different instance sets. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage deviation values.

Simulation of mesalazine solubility in the binary solvents at various temperatures

The present study aims are to develop general models based on Abraham solvation parameters and Hansen solubility parameters to predict mesalazine solubility in cosolvency systems at different temperatures. The Jouyban-Acree model as an accurate mathematical model is combined with physicochemical parameters of the Abraham solvation parameters and the Hansen solubility parameters to achieve the predictive cosolvency models. KAT-LSER model was also applied for the representation of mesalazine solubility data at 298.2 K. The applicability of the proposed methods was evaluated by calculating the overall mean relative deviations (MRDs %) values of the solubility data for mesalazine in 11 cosolvency systems and discussed. The trained models present a good estimation of the solubility behavior of mesalazine in various cosolvency systems, including aqueous and non-aqueous mixtures. The essential information presented by these models can be used in different stages of new drug and drug candidate discovery, development, and industrial procedures, so that they permit the scientist the choice the best solvent system for mesalazine with considering the fact that some solvents such as ethanol, propylene glycol, polyethylene glycols, N-methyl pyrrolidone, and carbitol are commonly used solvents in drug preparation procedure and other organic and volatile solvents are used in the extraction and purification step.

Dew point pressure of gas condensates, modeling and a comprehensive review on literature data

The accurate and in-time prediction of gas condensates dew point pressure (PDew) is of great importance regarding the technical and economic points of view for fluid characterization, reservoir performance calculations, planning the development of gas condensate reservoirs, and design and optimization of production systems. Although the laboratory tests provide the most accurate and reliable results, it is an expensive and time-consuming process sometimes associated with some errors. Artificial intelligence-based methods have emerged as promising tools in different aspects of engineering. In this study, after a thorough analysis of the gas condensate data samples, the application of different intelligent modeling is investigated. A databank of 721 data samples is gathered, and different intelligent methods approaches are used for modeling. The results of three different data mining methods are combined using Committee Machine Intelligent Systems (CMIS) in an attempt to receive more accurate results. Three different methods of arithmetic, geometric, and harmonic approaches are utilized to develop the CMIS model. It was concluded that the harmonic CMIS yields the best predictions by average absolute relative deviation (AARD) and R2 values of 3.456% and 0.9702, respectively. This novel CMIS model could successfully outperform all the developed initial models. Additionally, a literature review showed that the proposed model could outperform the previously published models including artificial intelligence, equation of state, and correlation-based method considering both prediction accuracy and data coverage.

Method for evaluation on energy consumption of cloud computing data center based on deep reinforcement learning

In view of the problem that the current method for evaluation on energy consumption of cloud computing data center has larger fluctuations of relative error and deviation value for evaluated power variation and greater evaluation training losses, this paper proposes an energy consumption evaluation method for cloud computing data center. Firstly, we build an energy consumption evaluation model, analyze the correlation between independent variables and dependent variables through multiple linear regression model and multiple nonlinear regression model, and then measure the accuracy of the evaluation results by using average relative deviation and relative deviation; The depth learning mechanism is introduced, and the depth residual network is used for iterative training of regression parameters to complete the mapping of regression parameters; Comprehensively obtain the regression parameters in the energy consumption model, put them into the memory playback pool, solve the energy consumption evaluation function according to the sub strategy parameters, and reconstruct the energy consumption model of cloud computing data center. The comprehensive evaluation of energy consumption of cloud computing data center is realized. The experimental results show that the energy consumption deviation of this method is small and stable. It can effectively reduce the relative error of evaluation power change and reduce the evaluation training loss.

Pollutant Flux Estimation of the Lijiang River Based on an Improved Prediction-Correction Method

Pollutant flux estimation and the analysis of flux variations are the basis for water quality assessment and water pollution control. At present, pollution flux estimation has certain shortcomings, such as a low frequency of water quality monitoring and inadequate calculation methods. To improve the rationality and reliability of river pollution flux estimation results, an improved prediction-correction pollution flux estimation method was developed by combining the LOADEST model and the Kalman filtering algorithm. By establishing the regression equation between pollutant flux and daily discharge, the predicted pollution flux procedure can be calculated using the LOADEST model. In a subsequent step, the pollutant flux is corrected based on the Kalman filtering algorithm. The improved method was applied to estimate the fluxes of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP) at the Guilin Section of the Lijiang River from 2010 to 2019. The estimated fluxes were in good agreement with the measured ones, with relative deviation values for COD, NH3-N, and TP of 2.27, 3.20, and 1.39%, respectively. The improved method can reasonably estimate fluctuations in river pollution fluxes without requiring more data. The results in the present study provide powerful scientific basis for pollutant flux estimation under low-frequency water quality monitoring.