Bayesian Hyperparameter Optimization Research Articles

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

A hybrid quantum ensemble learning model for malicious code detection

Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

Comparison of gridsearchcv and bayesian hyperparameter optimization in random forest algorithm for diabetes prediction

Diabetes Mellitus (DM) is a chronic disease whose complications have a significant impact on patients and the wider community. In its early stages, diabetes mellitus usually does not cause significant symptoms, but if it is detected too late and not handled properly, it can cause serious health problems. To overcome these problems, diabetes detection is one of the solutions used. In this research, diabetes detection was carried out using Random Forest with gridsearchcv and bayesian hyperparameter optimization. The research was carried out through the stages of study literature, model development using Kaggle Notebook, model testing, and results analysis. This study aims to compare GridSearchCV and Bayesian hyperparameter optimizations, then analyze the advantages and disadvantages of each optimization when applied to diabetes prediction using the Random Forest algorithm. From the research conducted, it was found that GridSearchCV and Bayesian hyperparameter optimization have their own advantages and disadvantages. The GridSearchCV hyperparameter excels in terms of accuracy of 0.74, although it takes longer for 338,416 seconds. On the other hand, Bayesian hyperparameter optimization has a lower accuracy rate than GridSearchCV optimization with a difference of 0.01, which is 0.73 and takes less time than GridSearchCV for 177,085 seconds.

Application of Ensemble Learning and Feature Selection Models in Energy Expenditure Estimation of Fitness Tracking Devices

The use of fitness tracking devices to monitor energy expenditure (EE) is becoming increasingly popular among consumers. However, the accuracy of EE estimation still needs to be improved due to the lack of accurate data filtering and more efficient data analysis models. To address this issue, we designed an energy expenditure estimation (EEE) model based on smart tracking devices. This paper proposes an algorithm for data reconstruction called PC-DF-RFECV: firstly, Pearson correlation analysis (PC) is used for initial data screening, then Feature Derivation (DF) is applied to reconstruct new features, and finally, the Recursive Feature Elimination algorithm based on cross-validation (RFECV) is employed to select the new features. Furthermore, the EEE model adopts a weighted average (Wei-Voting) strategy to enhance the robustness of the model by integrating the predictions of multiple base learners (XGBoost, LightGBM, Random Forest). During algorithm optimization, a Bayesian hyperparameter optimization technique is employed to fine-tune the model's hyperparameters. Empirical findings indicate that the EEE model, when applied to the dataset collected from motion tracking devices, attains an score of 0.9985, of 1.033, and of 0.865. These results demonstrate superior performance compared to current state-of-the-art research and conventional algorithms including random forests, gradient boosting, and bagging.

Enhanced Two-Stream Bayesian Hyper Parameter Optimized 3D-CNN Inception-v3 Based Drop-ConvLSTM2D Deep Learning Model for Human Action Recognition

Human Action Recognition (HAR) has grown to be the toughest and most attractive concern in the domains of computer vision, communication between a person and the surroundings, and video surveillance. In variation to the conventional methods that usually make use of the Long Short Term Memory model (LSTM) for training, this work designed dropout variant Drop-ConvLSTM2D, to provide more effectiveness in regularization for deep Convolution Neural Networks (CNNs).In addition, to speed up the runtime performance of the Deep Learning model, Bayesian Hyper Parameter Optimization (BHPO) is also introduced to autonomously optimize, the hyperparameters of the trained architecture. In this study, a two-stream Bayesian Hyper Parameter optimized Drop-ConvLSTM2D model is designed for HAR to overcome the current research deficiencies. In one stream, an Inception-v3 model extracts the temporal characteristics from the optical frames which are generated through the dense flow process. In another stream, a 3D-CNN involves the mining of the spatial-temporal characteristics from the RGB frames. Finally, the features of Inception-v3 and 3D-CNN are fused using which the Drop-ConvLSTM2D model is trained to recognize human behavior. On perceptive public video datasets UCF-101, and HMDB51, the quantitative assessments are conducted on the Drop-ConvLSTM2D BHPO model. For all hyperparameters, the built model explicitly obtains optimized values in this process, which can save time and improve performance. The experimental outcome shows that with a precision of at least 3%, the designed model beats the traditional two-stream model.

Multi-source driven estimation of earthquake economic losses: A comprehensive and interpretable ensemble machine learning model

Rapid and precise quantification of economic losses post-earthquake is critical for crafting informed disaster management strategies by governmental and insurance entities. This study introduces an ensemble model, constituted by Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost), tailored for quick and interpretative prediction of GDP-related seismic loss assessments, with Sichuan Province serving as the empirical backdrop. The ensemble approach, pre-trained for expediency, draws on a knowledge-driven selection of 30 features that span seismic risks, socio-economic variables, and exposure factors. Enhanced by feature scaling and Bayesian hyperparameter optimization, the model's efficacy is validated through 6 metrics such as R-Squared, MSE, and MAE etc. The incorporation of SHAP values further unravels the model's decision-making, providing transparency to the often opaque computations of machine learning. This methodology offers a scalable, interpretable framework that equips stakeholders with timely and accurate insights for risk mitigation, ultimately strengthening earthquake resilience.

Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessments of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainable goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the convolutional long short-term memory deep neural network (CLDNN) model and the transformer LSTM (temporal transformer) model have emerged as standout remaining useful life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable mean absolute error (MAE) of 84.012 and a mean absolute percentage error (MAPE) of 25.676. Similarly, the temporal transformer model exhibited a notable performance, with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved by applying Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were bench-marked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach

Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. Recently, convolutional neural networks (CNNs) have been carried out for medical diagnosis to obtain higher accuracy than standard machine learning algorithms and specialists' expertise. Therefore, the proposed approach involves using the Bayesian hyperparameter optimization with the CNN architecture for automatic diagnosis of ear imagery database including four classes: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). The suggested approach was trained using 616 otoscopic images, and the performance of this approach was assessed using 264 testing images. In this paper, the performance of ear disease classification was compared in terms of accuracy, sensitivity, specificity, and positive predictive value (PPV). The results produced a classification accuracy of 98.10%, a sensitivity of 98.11%, a specificity of 99.36%, and a PPV of 98.10%. Finally, the suggested approach demonstrates how to locate optimal CNN hyperparameters for accurate diagnosis of ear diseases while taking time into account. As a result, the usefulness and dependability of the suggested approach will lead to the establishment of an automated tool for better categorization and prediction of different ear diseases.

Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm

Abstract. The present work proposes a prediction model of significant wave height (SWH) and average wave period (APD) based on variational mode decomposition (VMD), temporal convolutional networks (TCNs), and long short-term memory (LSTM) networks. The wave sequence features were obtained using VMD technology based on the wave data from the National Data Buoy Center. Then the SWH and APD prediction models were established using TCNs, LSTM, and Bayesian hyperparameter optimization. The VMD–TCN–LSTM model was compared with the VMD–LSTM (without TCN cells) and LSTM (without VMD and TCN cells) models. The VMD–TCN–LSTM model has significant superiority and shows robustness and generality in different buoy prediction experiments. In the 3 h wave forecasts, VMD primarily improved the model performance, while the TCN had less of an influence. In the 12, 24, and 48 h wave forecasts, both VMD and TCNs improved the model performance. The contribution of the TCN to the improvement of the prediction result determination coefficient gradually increased as the forecasting length increased. In the 48 h SWH forecasts, the VMD and TCN improved the determination coefficient by 132.5 % and 36.8 %, respectively. In the 48 h APD forecasts, the VMD and TCN improved the determination coefficient by 119.7 % and 40.9 %, respectively.

Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimization

An accurate state estimator is critical to Battery Management Systems (BMSs). In more advanced BMSs, state estimators based on electrochemical-thermal battery models are preferred because they can predict the internal states of the battery. However, these physics-based models are computationally costly and time-demanding, preventing them from being directly used for real-time estimation. As an alternative approach, data-driven models, like Deep Neural Networks (DNNs), can be trained on datasets generated by electrochemical models. Increasing the accuracy of data-driven models can be achieved by applying more complex architectures of DNN. However, such DNNs are computationally time-demanding, which makes them inefficient for online state estimation algorithms. To address this issue, we have proposed a BiLSTM model as a state estimator whose hyperparameters are automatically optimized using a Bayesian Optimization (BO) framework. We demonstrate that by applying Bayesian inference, one can have a highly accurate state estimator while using a less complex DNN architecture which allows for being computationally efficient as well. The high accuracy of the BiLSTM model with the optimized combination of hyperparameters is demonstrated by the low Root Mean Squared Errors (RMSE) levels on the test data set.

Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization

Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization

Deep learning based solution of nonlinear partial differential equations arising in the process of arterial blood flow

The present work introduces a deep learning approach to describe the perturbations of the pressure and radius in arterial blood flow. A mathematical model for the simulation of viscoelastic arterial flow is developed based on the assumption of long wavelength and large Reynolds number. Then, the reductive perturbation method is used to derive nonlinear evolutionary equations describing the resistance of the medium, the elastic properties, and the viscous properties of the wall. Using automatic differentiation, the solutions of nonlinear evolutionary equations at different time scales are represented using state-of-the-art physics-informed deep neural networks that are trained on a limited number of data points. The optimal neural network architecture for solving the nonlinear partial differential equations is found by employing Bayesian Hyperparameter Optimization. The proposed technique provides an alternate approach to avoid time-consuming numerical discretization methods such as finite difference or finite element for solving higher order nonlinear partial differential equations. Additionally, the capability of the trained model is demonstrated through graphs, and the solutions are also validated numerically. The graphical illustrations of pulse wave propagation can provide the correct interpretation of cardiovascular parameters, leading to an accurate diagnosis and successful treatment. Thus, the findings of this study could pave the way for the rapid development of emerging medical machine learning applications.

Deep net classification of drum strike location with non-uniformmembrane tension

Tuning inaccuracies in snare drum membranes can cause inconsistent spectro-temporal changes (e.g., timbre differences) exacerbated by drumhead strike location. This experimental study assesses the potential for developing a deep net classifier for strike drum location, using a Non-UniformlyTuned Snare (NUTS) dataset, comprising 12,107 individual snare drum hits recorded in an anechoic chamber. Unique features of this dataset include its substantial size, additional novel features comprising physical attributes of the drum, and a performance-based classification schema. A series of deep net classifiers are trained using Bayesian hyperparameter optimization, on a subset of 8685 strikes of the NUTS dataset, whose acoustic features are extracted at nine different averaged time-windows(23–2000 ms). A comparison is then made against similar models supplemented by domain knowledge of 15 different combinations of non-uniform membrane tension, whose measures are recorded near the sixteen tuning lugs for each strike. Results found above chance model accuracies, and some performance benefits of including the tuning information as domain knowledge between equivalent trials. Shorter window sizes performed worse than longer time windows, and per-class F1-scores suggest complex spectro-temporal time variant changes caused by dis-uniform membrane tension, which could have implications for performers wishing to broaden the sonic capabilities of the instrument.

A machine learning study on the fatigue crack path of short crack on an α titanium alloy.

In the present study, a physics-informed neural network model based on Bayesian hyperparameter optimization is proposed for the prediction of short crack growth paths. A large number of cyclic loadings at a lower amplitude were applied to an α titanium sample by an ultrasonic fatigue machine to ensure a sufficient amount of data for machine learning. The grain size, grain orientation and grain boundary direction on the path, as well as crack growth direction, were selected as feature data for training the prediction model. The optimizations of the size ratio and the angle operation were conducted to compare different data processing methods, respectively. After evaluation, eventually, a model for predicting crack growth path is obtained with a reliable performance of 10% tolerance on the path angle at each grain boundary. And the prediction effect of the proposed model is better than that of some classic machine learning models and slip trace analysis. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance Via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically.This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance.The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

Bayesian Hyperparameter Optimization of stacked Bidirectional Long Short-Term Memory neural network for the State of Charge estimation

The battery industry has recently grown as a result of electric power adoption in many applications showing less reliance on fossil fuels. Accurate estimation of the State of Charge (SOC) is vital for optimal Battery Management System (BMS) operation to maintain the use of the battery within the safety limits. Data-driven methods require a careful hyperparameter selection as their results strongly depend on their choice. Heuristic methods involve manual tuning or exhaustive ways, which can be computationally demanding. Here a Bayesian Hyperparameter Optimization (BHO) with a Gaussian process is proposed, exploiting the benefits of the probabilistic approach to hyperparameter tuning to reduce the computational effort. For the accurate prediction of SOC, the BHO technique is implemented on a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network, incorporating a novel set of hyperparameters. The presented method is validated using two well-documented public datasets. Results are compared using datasets with different time granularity, revealing accuracy and computational differences while exhibiting superior yield capabilities compared to other state-of-the-art methods. To assess the computational effort associated with data size and model creation, a time granularity analysis was performed. The analysis demonstrated that decreasing the timestep resulted in reduced network creation time while maintaining similar error levels. Moreover, in order to ensure the comparability of the method, the metric of Floating-point Operations (FLOPs) was integrated to quantify the network’s computational volume. Finally, a sensitivity analysis performed on the time-series window span showed accuracy and computational load differences highlighting the need for caution and careful consideration.

An integrated machine learning framework with uncertainty quantification for three-dimensional lithological modeling from multi-source geophysical data and drilling data

Nowadays, it is commonplace for geological surveys to integrate multi-source geophysical data and drilling data in order to construct three-dimensional (3D) lithological models. In this context, manual translation of complex geophysical data into parameters used for 3D lithological modeling is challenging. Machine learning has recently shown great potential in 3D lithological modeling. However, the performance of machine learning algorithm is influenced by the imbalance in number of categories of lithological samples. In addition, the uncertainty associated with 3D lithological modeling by machine learning has rarely been quantified. This study presents a novel integrated machine learning framework to address the imbalance issue and to quantify uncertainty in 3D lithological modeling. As its novelty, our integrated machine learning framework can subdivide total uncertainty into aleatoric and epistemic uncertainties in the 3D lithological modeling procedure by stochastic gradient Langevin boosting. Another innovation of this study is the use of Bayesian hyperparameter optimization for automatic tuning of hyperparameters of the integrated machine learning framework. The 3D lithological and uncertainty modeling case study in the Jiaojia–Sanshandao gold district of China demonstrated the superiority of our proposed integrated machine learning framework. The proposed framework has great potential in integrating multi-source geophysical and drilling data for 3D lithological and uncertainty modeling in engineering geology.

Electrical load forecasting based on variable T-distribution and dual attention mechanism

Accurate load forecasting is essential for power system stability and grid dispatch optimization. However, this task is challenging due to the inherent instability and volatility of the load sequence. To address this problem, this paper proposes a novel load forecasting model that integrates periodicity detection, the variable t-distribution, and the dual attention mechanism. Periodicity detection has been incorporated into the self-attention mechanism for the first time, identifying the most significant period in the raw load sequence. Subsequently, the raw load sequence undergoes processing using empirical wavelet transform, resulting in a series of subsequences. A feature attention mechanism is then employed to extract relevant input features. Furthermore, a novel variable t-distribution distance matrix is introduced into the temporal self-attention mechanism, enhancing the influence of data at identical or nearby positions in other periods based on the length of the most significant period. This modification improves the capacity of the vanilla self-attention mechanism to effectively model the relationship between data at varying distances. The hyperparameters of the variable t-distribution are obtained through Bayesian hyperparameter optimization. Empirical evaluations on two datasets with distinct meteorological and load features show that the proposed model outperforms baseline models across all metrics.© 2017 Elsevier Inc. All rights reserved.

Multi-objective optimization of concrete mix design based on machine learning

This study proposes a multi-objective optimization (MOO) framework for optimizing concrete mixture proportions. Advanced methods such as K-fold cross-validation, Bayesian hyperparameter optimization, regression feature elimination and C-TAEA algorithm are used to develop suitable ML models. The results show that compared to the commonly used ML algorithms, the application of 5-fold cross-validation, hyperparameter and regression feature elimination significantly improves the prediction accuracy of compressive strength to 0.993 and binder intensity to 0.980. Moreover, quantitative correlations between different mix design parameters and concrete performances can be clearly observed. By using the proposed MOO model with C-TAEA algorithm, the errors between the predicted and tested values are only around 4%, exhibiting high prediction accuracy and the ability to solve multi-objective mixture optimization problems.

DNN model development of biogas production from an anaerobic wastewater treatment plant using Bayesian hyperparameter optimization

Deep neural networks have been regarded as accurate models to predict complex fermentation processes due to their capacity to learn from a high number of data sets via artificial intelligence. To enhance the performance of these models the main challenge is to select the appropriate hyperparameters, such as neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and others.This study aims to use a hybrid Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to predict biogas production from real wastewater treatment plant data using deep neural network machine learning (DNN) with optimized hyperparameters. Due to the high number of missing sample measurement records, the data preprocessing process has been performed in three sequential steps: the first step is to remove columns with a high percentage of missing values. The second step concerns removing rows with a high number of missing values. The remaining missing values in the dataset were removed in the third step using the dropna function from Pandas' library. Then, on the remaining data, three normalization techniques (MinMaxScaler, RobustScaler, and StandardScaler) were used for 16 relevant features of the anaerobic digestion process (AD) and compared with Non-Normalized data. The RobustScaler technique demonstrated good prediction capabilities of biogas volume produced. The maximum predicted volume was 2236.105 Nm3/d, with the coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively. This value remains slightly higher than the actual biogas volume produced in the wastewater treatment plant, which was 2131 Nm3/d. Adopting StandardScaler, MinMaxScaler, and Non-Normalized data provided statistical performance indicator values of (0.690; 170.518; 231.519), (0.679; 180.575; 235.648), and (0.647; 183.403; 247.203), respectively. These findings offer an autonomous strategy to monitor the effective operational variables of a large-scale digester, ensuring the stability of the complex fermentation process as well as the long-term sustainability and economic viability of the renewable energy approach in the sector of waste treatment.