Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow

Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling

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.

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil moisture is dramatic, and its time series data are highly noisy, nonlinear, and nonstationary, and thus hard to predict accurately. In this study, taking Jiangsu Province in China as an example, the data of 70 meteorological and soil moisture automatic observation stations from 2014 to 2022 were used to establish prediction models of 0–10 cm soil relative humidity (RHs10cm) via the extreme gradient boosting (XGBoost) algorithm. Before constructing the model, according to the measured soil physical characteristics, the soil moisture observation data were divided into three categories: sandy soil, loam soil, and clay soil. Based on the impacts of various factors on the soil water budget balance, 14 predictors were chosen for constructing the model, among which atmospheric and soil factors accounted for 10 and 4, respectively. Considering the differences in soil physical characteristics and the lagged effects of environmental impacts, the best influence times of the predictors for different soil types were determined through correlation analysis to improve the rationality of the model construction. To better evaluate the importance of soil factors, two sets of models (Model_soil&atmo and Model_atmo) were designed by taking soil factors as optional predictors put into the XGBoost model. Meanwhile, the contributions of predictors to the prediction results were analyzed with Shapley additive explanation (SHAP). Six prediction effect indicators, as well as a typical drought process that happened in 2022, were analyzed to evaluate the prediction accuracy. The results show that the time with the highest correlations between environmental predictors and RHs10cm varied but was similar between soil types. Among these predictors, the contribution rates of maximum air temperature (Tamax), cumulative precipitation (Psum), and air relative humidity (RHa) in atmospheric factors, which functioned as a critical factor affecting the variation in soil moisture, are relatively high in both models. In addition, adding soil factors could improve the accuracy of soil moisture prediction. To a certain extent, the XGBoost model performed better when compared with artificial neural networks (ANNs), random forests (RFs), and support vector machines (SVMs). The values of the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), Nash–Sutcliffe efficiency coefficient (NSE), and accuracy (ACC) of Model_soil&atmo were 0.69, 11.11, 4.87, 0.12, 0.50, and 88%, respectively. This study verified that the XGBoost model is applicable to the prediction of soil moisture at the provincial level, as it could reasonably predict the development processes of the typical drought event.

Evaporation from the water surface is among the main water losses from natural and artificial lakes and ponds. Air temperature (Ta), wind speed (va), relative humidity (RH), atmospheric pressure (pa), surface water temperature (Tw) and radiation (R) are among the physical controls of this process. In recent years, water temperature data have increasingly become available so that the question arises, if the measurement of radiation (which, in turn, affects water temperature) may still be required.The method employed in this study is modelling of daily evaporation by means of artificial neural networks (ANNs) of the multilayer perceptron type (backpropagation, one hidden layer), using varying sets of input variables. Evaporation data from a white Class A pan (Qiu et al., 2022) served as target (50 patterns of daily averages). A logistic activation function was used. Data records were divided 2:1 into training and testing sets, resp.Data were scaled to the interval between 0.1 and 0.9, and for each run (105 epochs) the root mean square error (RMSE) of the scaled output was computed.Learning rate (η), momentum (α) and number of hidden nodes were subject to optimization for three different sets of input variables. ANN runs of series S1 comprised Ta, va, RH, pa, Tw and incoming solar radiation (R) as inputs (6 in total). Series S2 and S3 were subsets of S1, with S2 using Ta, va, RH, pa and Tw as inputs. For the input data of Series S3, water temperature Tw  was replaced by radiation R.The neural networks achieved a fair representation of the evaporation data. Optimization yielded a minimum RMSE for Series S1 of 0.0514 and 0.0669 for training and testing, resp. (6 hidden nodes, η=0.009 and α=0.0). Using the same input variables with the exception of the incoming radiation (in total, therefore, 5 inputs) S2 reached a minimum training RMSE of 0.0557 and a minimum testing RMSE of 0.0887 (5 hidden nodes, η=0.012 and α=0.0).Series S3 with the 5 inputs Ta, va, RH, pa and R (with water temperature left out), finally achieved an RMSE of 0.0545 for training and 0.0775 for testing, resp. (6 hidden nodes, η=0.006 and α=0.2).Comparison of Series S2 and S3 shows that, in the case of the data set studied here, the ANNs including incoming radiation among their input variables (but excluding water temperature) outperformed those explicitly accounting for water temperature in lieu of radiation. Using both radiation and water temperature as inputs (S1) resulted in a notable improvement of the ANN output as compared to the runs with either of these variables not accounted for explicitly.ReferencesQiu, G. Y., Gao, H., Yan, C., Wang, B., Luo, J., & Chen, Z. (2022): An improved approach for estimating pan evaporation using a new aerodynamic mechanism model. Water Resources Research, 58, e2020WR027870. https://doi.org/10.1029/2020WR027870.

The Covid-19 pandemic that has hit the world, including Indonesia, has affected various aspects of life. This has also led to various new policies by the Indonesian government to limit the spread of Covid-19, one of which is the use of masks while on the move. This study aims to apply machine learning to government policy recommendations in allowing people to move without masks. The method used in this research is SVM (Support Vector Machine) for classification and ANN (Artificial Neural Network) for forecasting. The results of this study are MSE (Mean Squared Error) of 4.62 and MAE (Mean Absolute Error) of 0.97, and this study predicts the Covid-19 pandemic will decrease in February 2022 but will tend to rise again in March 2022. Next, in April 2022, cases will decrease. Based on the study results, the implementation of the policy can be carried out in April 2022 regarding community activities without masks.

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

This article discusses the applications of Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) strategies in a control context of a grid connected Wind Energy Conversion System (WECS), using a Doubly-Fed Induction Generator (DFIG). To rigorously explore the performance of the controllers, a systemic approach based on an ideal model and a nominal model of the wind turbine is used for the comparative analysis between PI, FLC and ANN. It turns out that the PI is less efficient than ANN and FLC. It turns out that the PI is less efficient than ANN and FLC. Whereas, for a high level of control, the FLC has better performance than the ANN. For a low level, the performance of the ANN is very slightly superior to the FLC. In addition, one notes a good rejection of disturbance and a good robustness with the FLC compared to the ANN. The latter has a Total Harmonic Distortion (THD) and cos ϕ ≈ 1 slightly better than the FLC. In short, the ANN and FLC present great advantages for the WECS and the results obtained are satisfactory. The platform used for modeling and simulation studies is MATLAB/Simulink.KeywordsWind Energy Conversion SystemFuzzy Logic Control (FLC)Artificial Neural Network (ANN)DFIGPower grid

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. As the computing power has substantially improved over last decade, the ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. Therefore, the application of ANN is more widespread in textiles and clothing industries over last decade. In this chapter, it aims to review current application of ANN in textiles and clothing industries over last decade. Based on literature reviews, the challenges encountered by ANN used in the industries will be discussed and the potential future application of ANN in the industries will also be addressed. The structure of this chapter comprises of seven sections. The first section includes background of ANN, importance of ANN in textiles and clothing and the arrangement of this chapter. In forthcoming three sections, they include review of applications of ANN in fibres and yarns, in chemical processing, and in clothing over last decade. Afterwards, challenges encountered by ANN used in textiles and clothing industries will be discussed and potential future application of ANN in textiles and clothing industries will be addressed in last section.

The present article aims to forecast streamflow by using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and autoregressive moving average (ARMA). For this purpose, the daily streamflow time series of two hydrometry stations of Hajighoshan and Tamar on Gorgan River are used for two periods of 1983–2007 and 1974–2007, respectively. Root mean square error (RMSE) and correlation coefficient (R) statistics are employed to evaluate the performance of the ANNs, ANFIS, and ARMA models for forecasting streamflow (1 day ahead). Comparison of the results reveals that the ANFIS model outperforms the ARMA model. Based on the results of validation stage, for the forecasting 1 day ahead streamflow, ANN with RMSE = 0.028 m3/s and R = 0.59 for the Hajighoshan station and RMSE = 0.013 m3/s and R = 0.44 for the Tamar station were found to be superior to the ANFIS with RMSE = 1.98 m3/s and R = 0.42 for the Hajighoshan station and RMSE = 2.18 m3/s and R = 0.22 for the Tamar station. In addition, for 2 day and 3 day ahead streamflow forecasts, the ANN models show superiority in the accuracy of forecasting streamflow compared with the ANFIS models.

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Accurate rainfall prediction is crucial for effective weather forecasting and climate modeling. This study aims to assess the effectiveness of a novel hybrid Statistical Downscaling-based Hidden Markov Model-Random Forest Model (SD-based HMM-RF) for rainfall prediction in Selangor, Malaysia. It also examines the best imputation methods for handling missing data, selects predictors for statistical downscaling by reducing dimensionality, and addresses uncertainties in zero-bounded rainfall data. The study utilized observed data (predictand) from 33 rainfall stations and atmospheric data (predictor), covering the period from 2008 to 2018. Seven imputation methods were tested: Mean Imputation (MeI), Median Imputation (MI), Expectation-Maximization (EM) Algorithm, Markov Chain Monte Carlo (MCMC), k-Nearest Neighbor (kNN), Non-iterative Partial Least Square (NIPALS), and Random Forest (RF). Principal Component Analysis (PCA) was used to manage high-dimensional data and select predictors, while HMM was applied to address uncertainties in zero-bounded rainfall data. Five hybrid models: Random Forest (SD-based HMM-RF), Support Vector Machine (SD-based HMM-SVM), Decision Tree (SD-based HMM-DT), k-Nearest Neighbors (SD-based HMM-KNN), and Artificial Neural Networks (SD-based HMM-ANN) were evaluated. Performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Forecast Error (MFE), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Rank Correlation Coefficient (ρ), were used to identify the most accurate rainfall prediction model. MI emerged as the best-performing method, achieving the lowest RMSE (0.146) and MAE (0.021), along with the highest NSE (1.000) and Pearson correlation coefficient, r (1.000) for all stations. PCA identified five principal components (PC1–PC5) with a cumulative variance of up to 93.254%. HMM analysis determined that three hidden states (K = 3) at iteration 2000 were optimal, with the lowest Bayesian Information Criterion (BIC) of 260,018.30. Among the models, SD-based HMM-RF consistently outperformed others, showing superior accuracy and reliability in rainfall prediction, as indicated by its RMSE (2.298), NSE (0.752), MAE (1.900), near-zero MFE (-0.049), KGE (0.526), and ρ (0.898). By improving rainfall prediction accuracy, the novel hybrid model can enhance early warning systems, inform infrastructure planning, and reduce the economic impact of future flooding events, thereby contributing to more resilient urban development in the region.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

38 - Artificial Neural Network Applications in Power Electronics and Electrical Drives

Poor urban air quality due to high concentrations of particulate matter (PM) remains a major public health problem worldwide. Therefore, research efforts are being made to forecast ambient PM concentrations. In this study, artificial neural networks (ANNs) were employed to generate models forecasting hourly PM10 concentrations 1–6 h ahead, involving 3 measurement locations in the Tricity Agglomeration, Poland. In Poland, the majority of high PM concentration cases occurs in winter due to coal combustion being the main energy carrier. For this reason, the present study covers only the periods of the winter calendar (December, January, February) in the period 2002/2003–2016/2017. Inputs to the models were the values of hourly PM10 concentrations and meteorological factors such as air temperature, relative humidity, air pressure, and wind speed. The results of the neural network models were satisfactory and the values of the coefficient of determination (R2) for the independent test set for three sites ranged from 0.452 to 0.848. The values of the index of agreement (IA) were from 0.693 to 0.957, the fractional mean bias (FB) values were 0 or close to 0 and the root mean square error (RMSE) values varied from 8.80 to 23.56. It is concluded that ANNs have been proven to be effective in the prediction of air pollution levels based on the measured air monitoring data.

Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality, and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It, therefore, falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews the application of ANNs in construction activities related to the prediction of costs, risk, and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting inadequate input information. It was seen that most of the investigators used the feed forward back propagation type of the network; however, if a single ANN architecture was found to be insufficient, then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.