Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm

Accurate sediment discharge estimation is vital for designing and managing hydraulic structures. This study aimed to develop a regional model for estimating suspended sediment yield (SSY) in ungauged catchments by examining topographic, geomorphological, land use, climatic, and hydrological variables. However, including all variables may hinder model performance. To identify the most influential factors, various data reduction methods, namely principal component analysis (PCA), the gamma test (GT), classification and regression trees (CART), and stepwise regression (SR), were used. Multiple linear regression (MLR) and artificial neural networks (ANN) were used to develop the SSY estimation model. The findings indicated that drainage area and overland flow length were the most influential variables on SSY. The model showed high accuracy, with an R2 of 99.9% during calibration and 82% during validation. The GT-ANN model outperformed other models, indicating that AI technologies can effectively be utilized in the evaluation of SSY dynamics in ungauged catchments.

Estimation of annual suspended sediment yield from a Japanese cypress ( Chamaecyparis obtusa) plantation considering antecedent rainfalls

Evaluation of data driven models for river suspended sediment concentration modeling

The test- day model is a method of choice for the study of milk yield traits and this method is very important in countries like India where herd size is generally smaller and lacking well-established milk recording system. The present study was aimed to predict first lactation milk yield on the basis of test day yield in Gir cows and comparison was made between the relative efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models. First lactation records of 513Gir cows sired by 75 bulls spread over a period of 34 years (1981to 2014), maintained at Cattle Breeding Farm, Junagadh were used for the study. The data of monthly test-day milk yield (MTMY)was divided into seven sets. ANN was used with backpropagation Bayesian regularization (BR) algorithm and MLR was used with backward elimination method. The accuracy of prediction of first lactation milk yield in MLR was lower than the accuracy of ANN in all the test data sets. The Root Mean Square Errors (RMSE) of prediction were lower in ANN as compared to MLR. The optimum equation had total four variables (test days)viz. TD2 to TD5 for prediction of First Lactation 305-Days Milk Yield (FL305DMY). This equation gave an accuracy of prediction of 76.02% by MLR and 87.69% by ANN model till 125th days of lactation i.e. 5th monthly test day.

Estimating olive (Olea europaea L.) leaf area is an important aspect of monitoring plant health and evaluating growth processes in agriculture. Accurate estimation of leaf area allows for a better understanding of processes such as water and nutrient utilization, photosynthesis efficiency, respiration, and yield potential. This study aims to determine the most accurate, easy, and reliable leaf area estimation model using the geometric properties (length and width) of olive leaves. Additionally, the predictive performances of multiple linear regression (MLR) and artificial neural network (ANN) were compared. A total of 1320 leaf samples collected from 22 olive cultivars were used in the study. Leaf length and width were taken as input parameters, and both MLR and ANN models were developed for each cultivar. Both multiple linear regression (MLR) and artificial neural network (ANN) models demonstrated high predictive accuracy for olive leaf area estimation across 22 cultivars. The MLR models explained up to 96% of the variation in leaf area using leaf length (LL) and leaf width (LW), with low root mean square errors, indicating strong reliability. When cultivar identity was modeled as a categorical factor through dummy encoding, the model captured significant cultivar-specific effects without altering the overall predictive performance. The ANN models achieved slightly higher accuracy, with determination coefficients exceeding 0.99 and minimal prediction errors, confirming their superior ability to model nonlinear relationships. Across both approaches, leaf width contributed more strongly to leaf area than leaf length. Cultivar-specific differences were statistically significant for only a few genotypes, while most cultivars exhibited comparable patterns after adjustment for multiple testing. In conclusion, both MLR and ANN models demonstrated high accuracy in predicting olive leaf area, with ANN models showing slightly superior performance. However, MLR models also yielded highly reliable results, indicating that both approaches are viable for practical applications in olive cultivation. These predictive models can be effectively used for rapid, non-destructive phenotyping, growth monitoring, and precision management in olive breeding and production systems.

Spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring CO2 and preventing soil erosion strategies within the landscape, regional, and global scale. The objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain attributes. A study area of 24 km2 in hilly regions of Zargham Ababd in south of Semirom under natural rangeland uses, was selected and then 125 soil samples (0-10 cm depth) were collected. Soil organic carbon was measured for the collected soil samples. Topographic attributes were calculated by a digital elevation model with 10 m spacing. Finally, multiple linear regression (MLR) analysis and ANN models were developed for soil organic carbon estimation in the study area and then the developed modeless were validated by additional samples (25 points). The results showed that the MLR and ANN models explained 60 and 89 % of the total variability of SOC, respectively, in the study area using terrain attributes. Sensitivity analysis based upon the ANN models, revealed that the profile curvature, stream power index, slope, sediment transport index, wetness index, plan curvature and aspect were identified as the important topographic attributes influencing the SOC distribution within the selected hillslope. The overall results indicated that topographic attributes and hydrological process control a significant variability of SOC. Prediction of the statistical studied models in the study area resulted in mean error and root mean square error values of 0.25, 0.3 in MLR equation and 0.006, 0.027 in ANN, respectively. Therefore, the ANN model could provide superior predictive performance when compared with developed MLR model. Keywords: Soil organic carbon, Terrain attributes, Linear regression, Artificial neural network

Explanation of climate and human impacts on sediment discharge change in Darwinian hydrology: Derivation of a differential equation

Most soil‐erosion research has been undertaken at small spatial scales, i.e. from small erosion plots over field parcels to small catchments of a few hectares. On the other hand, sediment‐yield data for several large river basins (>1000 km2) are also available. Little information exists, however, on the link between soil‐erosion processes on hillslopes and yields of suspended sediment in large rivers. Data on sediment yield for smaller catchments (1–50 km2) are therefore needed. Such data can be obtained by different measuring methods of which sediment rating curves or river sampling are the most common. In this study, however, the possibilities and limitations of the use of sedimentary deposits in small ponds (<1 ha) to assess sediment yields is investigated. In small catchments (<50 km2), many small flood retention ponds, ponds for irrigation purposes or farm ponds already exist and no expensive monitoring equipment needs to be installed to assess sediment yield. This technique, however, also has a few limitations. Volumetric sediment deposition rates measured in these ponds need to be converted to sediment masses using representative values of the dry sediment bulk density. Furthermore, measured sediment masses need to be corrected for the trap efficiency of the pond otherwise sediment yield will be underestimated. These conversions and corrections are subject to errors. For 21 catchments in central Belgium, the mean error on computed sediment yield, using sediment deposits in small ponds, was estimated to range between 40 and 50 per cent. This error more or less corresponds to the error when using sediment rating curves and suggests that this technique can be a valuable alternative to provide data on the regional variations in sediment yield. Copyright © 2002 John Wiley & Sons, Ltd.

This study aims to create an effective forecasting model in predicting sales of car products in the B2B segment (Business to Business) to obtain estimates of product sales in the future. This research uses multiple linear regression and artificial neural networks that are optimized by genetic algorithms. Forecasting factors for car sales are generally issued by total national car sales, the Consumer Price Index, the Consumer Confidence Index, the Inflation Rate, Gross Domestic Product (GDP), and Fuel Oil Price. The author has also gotten the factors that play a role in the sale of B2B segment by diverting the survey to 106 DMU (Decision Making Unit) who decide to purchase cars in their company. Then we evaluate the results of the questionnaire in training data and simulations on the Artificial Neural Network. Optimized Artificial Neural Networks with Genetic Algorithms can improve B2B segment car sales' accuracy when comparing error values in the ordinary Artificial Neural Network and Multiple Linear Regression.

Different chemical attributes, measured via total soluble solids (TSS), acidity, vitamin C (VitC), total sugars (Tsugar), and reducing sugars (Rsugar), were determined for three groups of citrus fruits (i.e., orange, mandarin, and acid); each group contains two cultivars. Artificial neural network (ANN) and multiple linear regression (MLR) models were developed for TSS, acidity, VitC, Tsugar, and Rsugar from fresh citrus fruits by applying different independent variables, namely the dimensions of the fruits (length (FL) and diameter (FD)), fruit weight (FW), yield/tree, and soil electrical conductivity (EC). The results of ANN application showed that a feed-forward back-propagation network type with four input neurons (Yield/tree, FW, FL, and FD) and eight neurons in one hidden layer provided successful modeling efficiencies for TSS, acidity, VitC, Tsugar, and Rsugar. The effect of the EC variable was not significant. The hyperbolic tangent of both the hidden layer and the output layer of the developed ANN model was chosen as the activation function. Based on statistical criteria, the ANN developed in this study performed better than the MLR model in predicting the chemical attributes of fresh citrus fruits. The root mean square error of TSS, acidity, VitC, Tsugar, and Rsugar ranged from 0.064 to 0.453 and 0.068 to 0.634, respectively, for the ANN model, and 0.568 to 4.768 and 0.550 to 4.830, respectively, for the MLR model using training and testing datasets. In addition, the relative errors obtained through the ANN approach provided high model predictability and feasibility. In chemical attribute modeling, the FD and FL variables exhibited high contribution ratios, resulting in a reliable predictive model. The developed ANN model generally showed a good level of accuracy when estimating the chemical attributes of fresh citrus fruit.

Genetic algorithm (GA) - Artificial neural network (ANN) modeling for the emission rates of toxic volatile organic compounds (VOCs) emitted from landfill working surface

This study aims to compare linear and nonlinear prediction methods in predicting the Kovats retention index of 126 compounds extracted from the Lippia origanoides plant. The retention index of each compound has been predicted based on its molecular descriptors. There have been 189 molecular descriptors for each compound in this study, and the best descriptors have been selected using the Genetic Algorithm (GA). It has succeeded in selecting the best five descriptors used to build the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) model. MLR has obtained R2 of 0.959, 0.946, 0.955, and a Root Mean Square Error (RMSE) of 48.00, 50.84, 47.19 on training, validation, and testing, respectively. Meanwhile, ANN has obtained R2 of 0.963, 0.947, 0.962, and an RMSE of 45.45, 50.59, 43.20, respectively. Compared to MLR, there has been an increase in the ANN model's performance, with an increase in R2 of 0.004, 0.001, and 0.007 and a decrease in RMSE of 2.55, 0.25, and 3.99. Based on the prediction results obtained, it is known that in this case, the ANN method can provide better predictive results than MLR.

This paper presents the application of an artificial neural network (ANN) model for predicting the penetration depth under projectile impact in ultra-high-performance concrete (UHPC) targets containing steel fibres. Despite the availability of a large number of existing empirical models, the prediction of penetration depth remained inconclusive, partly due to the phenomenon's complexity and partly due to the limitation of statistical regression. From the results of this study, it is evident that the ANN model is capable of predicting the penetration depth of UHPC more accurately than other machine-learning (ML) models (linear regression, decision tree regression and random forest regression) and empirical formulae. The ANN model achieved a lower root mean square error (RMSE) of 11.68 compared to the other ML models (RMSE: 16.66–19.74) and empirical equations (RMSE: 25.17–53.42), when applied to the test data set. The velocity, impact energy, diameter of the projectile and thickness of the UHPC targets are the most significant parameters (P-value <5%) for predicting the penetration depth using ANN and multiple linear regression models.

A robust method to estimate foliar phosphorus of rubber trees with hyperspectral reflectance

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.