Mean Absolute Percentage Error Values Research Articles

ADDITIVE HOLT-WINTERS METHOD FOR FORECASTING GROSS REGIONAL DOMESTIC PRODUCT AT CONSTANT PRICES OF EXPENDITURE OF WEST SUMATRA

Regional disparities often pose significant challenges, with some areas experiencing rapid economic growth while others lag behind. An essential macro benchmark to gauge the success of development initiatives is economic growth, as indicated by changes in a region's Gross Regional Domestic Product (GRDP). Using GRDP at constant prices (GRDP CP) helps eliminate the impact of price fluctuations, focusing on real increases in production activities. Expenditure GRDP reflects the value of goods and services produced within a region and consumed by the community. In the case of West Sumatra, the GRDP CP expenditure data reveals simultaneous seasonal and trend elements. The seasonal pattern, occurring quarterly each year, exhibits an additive seasonal effect. The Additive Holt-Winters method has been proven effective for data containing seasonal patterns with constant seasonal variation (additive) and linear trends, where the level, trend, and seasonal pattern can change. The data used is secondary data of GRDP CP of expenditure quarterly of West Sumatra in 2010 - 2022 obtained from the official website of the Indonesian Central Bureau of Statistics. According to research findings, the GRDP CP expenditure with a Mean Absolute Percentage Error (MAPE) value of 1.03% for quarter I to quarter IV in 2023 are Rp46,284,010.59, Rp46,472,223.99, Rp47,512,197.79, and Rp48,445,184.94, respectively. This suggests that the model equation performs exceptionally well predicting future economic trends.

PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.

ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION

Climate change is a complex process that has far-reaching consequences for daily living. Temperature is one of the climatic features. Knowing its future value through a forecasting model is critical, as it aids in earlier strategic decision-making. Without considering spatial factors, this study investigates an Air Temperature variable forecasting. Weighted Fuzzy Time Series (WFTS) is one of the forecasting techniques. Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. Therefore, this research investigates the interval length and the Order length of the WFTS through the Particle Swarm Optimization (PSO) approach. The variable used is the air temperature in Malang, Indonesia. The dataset is taken from BMKG-Indonesia. The forecasting performance of classical WFTS is enhanced by setting an appropriate order level and employing Particle Swarm Optimization (PSO) to determine the optimal interval fuzzy length. As indicated by the Evaluation matrices in the result section, the proposed optimization overtaken the classical WFTS in term of accuracy. The evaluation indicates a Mean Absolute Percentage Error (MAPE) value of 1.25 and a Root Mean Square Error (RMSE) of 0.32 for the Proposed model. In contrast, the classical WFTS demonstrates a MAPE of 2.26 and RMSE of 0.58. The implementation of the PSO provides solid insights for Air temperature forecasting accuracy.

A Case Study: Comparison of LSTM and GRU Methods for Forecasting Oil, Non-Oil, and Gas Export Values in Indonesia

This study explores the forecasting of Indonesia’s oil, non-oil, and gas export values, highlighting its critical role in supporting national economic growth. Given the inherent volatility in export values, accurate forecasting is vital for informed economic decision-making. The research employs Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, both well-regarded for their ability to handle sequential data and complex temporal patterns. Model performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The findings indicate that although both models produced nearly identical MAPE values of 99.99% across the oil, non-oil, and gas sectors, the GRU model outperformed the LSTM model with RMSE values of 0.0655 for oil and gas exports and 0.0697 for non-oil and gas exports. Moreover, the GRU model’s forecasts align closely with data from the Central Bureau of Statistics (BPS), which reported an 11.33% decline in Indonesia’s export values by the end of 2023. These results suggest that the GRU model not only offers greater accuracy but is also applicable to other economic forecasting contexts, such as exchange rate and inflation predictions, thereby enhancing economic policy-making.

Application of Markov Chains and Artificial Neural Networks in the prediction of Hepatitis Cases in the southern states of Brazil

Viral hepatitis is a disease caused by five known types of virus and can be chronic or acute. This study collected historical data on hepatitis in the southern states of Brazil and applied Markov Chains as an input parameter for predicting cases, using Artificial Neural Networks (ANN). A comparison was then made between the method that includes the two methodologies and the prediction made by ANN using only the data from the historical series. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were calculated to determine the best forecasting model for each state. It was not possible to determine a configuration that simultaneously presented the best MAE and MAPE values for each state, but it was found that the lowest errors were obtained by using Markov Chains as an information generator for the ANN models, with a MAPE of 4.45% using the Levenberg-Marquardt training algorithm, with a delay equal to 3 and a number of neurons equal to 60.

Comparison of Triple Exponential Smoothing and ARIMA in Predicting Cryptocurrency Prices

Cryptocurrency has emerged as a prominent digital asset over the past decade, but its high price volatility presents significant challenges for investors. This study evaluates and compares the effectiveness of the Triple Exponential Smoothing (TES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting the prices of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP). TES models trends and seasonality in time series data, while ARIMA captures autoregressive patterns and moving averages. The dataset is split into 80% for training and 20% for testing, with performance evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). TES outperforms ARIMA in predicting Bitcoin and Binance Coin, achieving MAPE values of 10.38% and 13.81%, and RMSE values of 3,985.55 and 41.28, respectively. However, ARIMA shows better performance for Ethereum, Solana, and Ripple, with MAPE ranging from 8.78% to 32.84% and RMSE between 0.08 and 204.59. Notably, Ethereum has the lowest MAPE at 8.78%, while Ripple exhibits the smallest RMSE at 0.08. These findings suggest that TES is more suitable for cryptocurrencies with relatively stable price patterns, while ARIMA is better adapted to forecasting highly volatile assets. This research underscores the importance of selecting forecasting models based on the specific characteristics of each cryptocurrency

State-of-health estimation and classification of series-connected batteries by using deep learning based hybrid decision approach

In rechargeable battery control and operation, one of the primary obstacles is safety concerns where the battery degradation poses a significant factor. Therefore, in recent years, state-of-health assessment of lithium-ion batteries has become a noteworthy issue. On the other hand, it is challenging to ensure robustness and generalization because most state-of-health assessment techniques are implemented for a specific characteristic, operating situation, and battery material system. In most studies, health status of single cell batteries is assessed by using analytical or computer-aided deep learning methods. But, the state-of-health characteristics of series-connected battery systems should be also focused with advances of technology and usage, especially electric vehicles. This study presents a data-driven, deep learning-based hybrid decision approach for predicting the state-of-health of series-connected lithium-ion batteries with different characteristics. The paper consists of generating series-connected battery degradation dataset by using of some mostly used datasets. Also, by employing deep learning-based networks along with hybrid-classification aided by performance metrics, it is shown that estimating and predicting the state-of-health can be achieved not only by using sole deep-learning algorithms but also hybrid-classification techniques. The results demonstrate the high accuracy and simplicity of the proposed novel approach on datasets from Oxford University and Calce battery group. The best estimated mean squared error, root mean square error and mean-absolute percentage error values are not more than 0.0500, 0.2236 and 0.7065, respectively which shows the efficiency not only by accuracy but also error indicators. The results show that the proposed approach can be implemented in offline or online systems with best average accuracy of 98.33 % and classification time of 58 ms per sample.

Evaluation of Trend Models Performance and Forecasting Onion Production: A Comparative Study

Onions, a significant spice crop in Bangladesh, frequently experience price surges due to an imbalance between demand and supply, compelling the government to import onions. The volatility of the onion market in Bangladesh and the measures needed to stabilize the situation require intensive policy attention. Therefore, this study was carried out to forecast the onion production over time and to evaluate the efficacy of various trend models. The secondary data was collected from the Food and Agriculture Organization's Statistical Database (FAOSTAT) from 1972 to 2022. The autoregressive integrated moving average (ARIMA) model was used to predict the onion production over time. Additionally, metrics such as modified and the coefficient of determination were used to evaluate the effectiveness of eight trend models comprehensively. The findings indicate that the cubic model is superior to others since it has larger and adjusted values. The models' accuracy is also determined utilizing mean absolute percentage error (MAPE) and mean absolute deviation (MAD) metrics. The cubic trend model exhibits lower MAPE and MAD values of 29.43% and 80638.67, respectively. Therefore, it is concluded that the ARIMA (2, 2, 2) model is the best model for forecasting the annual production of onion in Bangladesh. A five-year trend analysis spanning from 2023-2027 indicates an upward trend. Such a result offers a critical insight for stakeholders and policymakers in formulating sustainable onion production strategies. J Bangladesh Agril Univ 22(3): 386-395, 2024

Analysis of carbon emission characteristics and establishment of prediction models for residential and office buildings in China

Intensifying environmental issues make carbon reduction crucial, especially in the construction industry. Embodied carbon emissions (ECE) in buildings characteristically involve concentrated, intensive emissions over a short period. Implementing energy-saving and carbon-reducing technologies has raised ECE rates. Therefore, analyzing carbon emission (CE) characteristics during the production and construction phases is necessary. Currently, essential data on ECE is lacking, the calculation methods are complex, and ECE's characteristics and influential factors in residential and office buildings remain unclear. Moreover, there is a lack of prediction models for ECE in the construction phase that apply to current construction methods and CE factors. The research on prediction models for ECE has primarily focused on the production phase but has paid little attention to the contribution of non-structural materials to embodied carbon (EC). In this study, reinforced concrete residential and office buildings in hot summer and cold winter regions (located in the southeast part of mainland China, with a subtropical monsoon climate) were considered typical types, and the CE characteristics of two main phases, the production and construction phases, were analyzed. Based on critical features, such as the number of floors and the basement, prediction models for ECE in residential and office buildings were established at the design stage. The models were validated with mean absolute percentage error (MAPE) values below 4%, and coefficient of variation (root mean square error) (CV [RMSE]) values less than 0.02. The prediction models enabled a rapid estimation of ECE, encouraging designers to consider EC in the early design stages.

Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method

This research aims to implement advanced data mining techniques for the classification of vertigo disorders using the Support Vector Machine (SVM) method. Vertigo, characterized by a spinning sensation, can be triggered by various factors such as nervous system disorders and inner ear infections. With the rising prevalence of vertigo patients, there is a pressing need for more effective and efficient diagnostic tools. This study was conducted at Puskesmas Jangka in Bireuen Regency, involving the collection of vertigo patient data from the years 2023-2024. The collected data underwent a comprehensive preprocessing pipeline, including data cleaning, partitioning into training and testing datasets, and subsequent implementation of the SVM algorithm. The performance of the model was evaluated using the Mean Absolute Percentage Error (MAPE), resulting in a MAPE value of 28.47%.

Crop cover identification based on different vegetation indices by using machine learning algorithms

In this article, three different indices NDVI (Normalized Difference Vegetation Index), BNDVI (Blue Normalized Difference Vegetation Index) and GNDVI (Green Normalized Difference Vegetation Index) are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.

Double Moving Average and Double Exponential Smoothing Method in Sales Forecasting

At present, predictions concerning sales are predominantly based on historical sales data, a practice that frequently yields inaccurate results. Such inaccuracies can lead to substantial financial losses, compelling organizations to lower the capital expenditures associated with certain products to offset these losses. This issue primarily arises from the failure to employ an appropriate forecasting methodology, resulting in estimations that lack reliable analytical foundations. This research aims to evaluate the efficacy of the Double Moving Average method compared to the Double Exponential Smoothing technique for sales forecasting, specifically through a case study involving herbal products. This study also seeks to analyze and compute the Mean Absolute Percentage Error (MAPE) for each forecasting method based on prior observations and research. The analysis draws on a dataset comprising 200 sales transactions from the five top-selling products collected between April 2022 and April 2024. The outcomes of this investigation provide MAPE values derived from the sales data, followed by a comprehensive summation of the calculated MAPE for each method. For June 2024, the results recorded are slightly higher in some cases. For example, the DES result for HNI Eucalyptus Oil was 42.65 with a MAPE of 0.46, while the DMA was 44.44 with a MAPE of 0.44.

Application of artificial neural network for peak load forecasting in 150 kV Semarang power system

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544).

Damage of thin target plates impacted by conical projectiles with varying apex angles in ballistic application: experimental, FEA and ANN integrated approach

In the current study, an experimental setup consisting of smoothbore 30-caliber powder gun was employed to launch spherical projectiles (3 g/ϕ9) in the ballistic range of velocities from 500 to 1700 m/s and obliquity (0, 33 and 65°), impacting 2 mm thin Steel 1006 target plates. Crater dimensions (major and minor dia.) obtained from series of FE simulations run for the above configuaration was validated by corresponding experimental data. Subsequently, a fullscale 3D FE model considering 8-noded hexahedral elements was used to discretize SS304 conical projectiles (replacing spherical projectile) with various apex angles viz. 40°, 60°, 80° and 100° impacting on single (3 mm) and double layer (1.5 mm each) steel 1006 targets (replacing 2 mm target) in LS dyna. The material behavior was characterized using J–C strength and damage models, along with Gruneisen Equation of State. An erosion algorithm was used in the explicit FE code LS-DYNA to remove undesirable elements. In the next stage, Adam and Nadam optimizers have been employed in ANN models developed using Python code within the Tensor-Flow framework. The Keras Tuner library in the Tensor-Flow framework was used for hyper parameter tuning. The ANN models trained using simulation data successfully predicted the residual KE of conical projectiles with intermediate apex angles of 90°, 70°, and 50° across twelve different impact scenarios. The models with Adam and Nadam optimizers achieved mean squared error (MSE)/coefficient of determination (R2)/mean absolute percentage error (MAPE) values of 109.44/0.998/6.72 and 91.19/0.998/7.76, respectively.

Gap filling of missing satellite data from MODIS and CMEMS for chlorophyll-a in the waters of Aceh, Indonesia

The motivation behind our study is to identify a robust method to enhance the accuracy of missing data, particularly chlorophyll-a data, which often goes undetected due to various factors. This study analyzes chlorophyll-a concentrations and sea level changes due to tides using three methods: Linear Interpolation, Fillgaps, and Modified Fillgaps. Two experiments were conducted: Experiment I involved random data removal (60% and 70%), and Experiment II combined sequential and random data removal (25% sequentially on the right, 35% and 45% randomly on the left). In Experiment I, the Modified Fillgaps method showed high correlation coefficients (up to 0.96) between original and reconstructed data, demonstrating its effectiveness in accurately filling significant data gaps. This method also exhibited low Root Mean Square Error and Mean Absolute Error values, confirming its predictive precision. In Experiment II, despite structured and realistic data loss patterns, the method maintained high correlation and low prediction errors, with low Normalized Root Mean Squared Error and Mean Absolute Percentage Error values, further validating its reliability. Additionally, the method excelled in two-dimensional chlorophyll-a maps, outperforming Linear Interpolation and Fillgaps methods in scenarios with 50% and 60% data loss, achieving higher correlation and lower prediction errors. These findings are crucial for environmental and climatological studies relying on satellite-derived data, confirming the Modified Fillgaps method as the most reliable and effective for handling significant data loss in chlorophyll-a map analyses. Future research should explore its application to other environmental data types and more complex data loss patterns.

A Bayesian Approach for Modeling and Forecasting Solar Photovoltaic Power Generation.

In this paper, we propose a Bayesian approach to estimate the curve of a function f(·) that models the solar power generated at k moments per day for n days and to forecast the curve for the (n+1)th day by using the history of recorded values. We assume that f(·) is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values f(t)=f(1),…, f(k). An advantage of this approach is that we may estimate the curves of f(·) and fn+1(·) as "smooth functions" obtained by interpolating between the points generated from a k-variate normal distribution with appropriate mean vector and covariance matrix. Since the joint posterior distribution for the parameters of interest does not have a known mathematical form, we describe how to implement a Gibbs sampling algorithm to obtain estimates for the parameters. The good performance of the proposed approach is illustrated using two simulation studies and an application to a real dataset. As performance measures, we calculate the absolute percentage error, the mean absolute percentage error (MAPE), and the root-mean-square error (RMSE). In all simulated cases and in the application to real-world data, the MAPE and RMSE values were all near 0, indicating the very good performance of the proposed approach.

Abnormal high water temperature prediction in nearshore waters around the Korean Peninsula using ECMWF ERA5 data and a deep learning model

The abnormally high-water temperature (AHWT) phenomena have caused the mass stranding of farmed fish in the Korean coastal waters, leading to a substantial monetary loss in recent decades. It is most important to predict the HWT occurrence and take responsive measures before the HWT arrival to prevent such loss, we proposed a methodology to predict HWT occurrences using a deep-learning technology. Firstly, we trained a long short-term memory (LSTM) deep-learning model using the sea surface temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 product to estimate future water temperature in advance. Secondly, we used the estimated water temperature data to predict HWT occurrences from 1 day to 7 days later. We computed root mean square error (RMSE), mean absolute percentage error (MAPE) metrics, and F1-scores to evaluate the accuracy of the proposed LSTM model. In the cases of 1-day and 7-day water temperature predictions, RMSE and MAPE values between the estimated data and the sea-truth data were 0.293 degrees Celsius with 1.313 % and 0.854 degrees Celsius with 4.175 %, respectively. The F1-scores of the classification algorithm of 1- and 7-day HWT predictions were 0.96 and 0.74, respectively. This study contributes to developing measures to reduce the monetary loss of HWT damage on fish farms.

Accuracy of COVID-19 Prediction Modeling Techniques

The unprecedented impact of the COVID-19 pandemic has revealed that forecasting capability is critically needed in making strategic decisions and formulating reasonable countermeasures. This study aimed to assess the predictive accuracy in forecasting the numbers of COVID-19 cases using Thailand’s national COVID-19 surveillance database from January 2020– June 2021 based on three analytical models: a susceptible-exposed-infectious-recovery compartmental model, an auto-regressive integrated moving average model, and a long short-term memory (LSTM) network model. All forecasting methods had model parameters adjusted weekly according to the most recent situation and predictive accuracy measures, including the mean absolute percent error (MAPE). We found that the MAPE values ranged from 19.65%–22.54%, 28.95%–32.35%, 47.78%–53.55%, and 75.03–84.91% for forecasting one, two, four, and eight weeks ahead, respectively. Among the three models, the LSTM model had slightly higher accuracy than the other two models within the same forecasting range. These prediction models can be used for short-range forecasts in other similar settings while long-range forecasting requires monitoring and updating periodically.

Modeling and Forecasting High Frequency Currency Exchange Rates. A Comparative Study of ARIMA, ANN, and Hybrid ARIMA-ANN Models

Aims/ Objectives: The study develops comparative results on the modeling and prediction performance of the autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and the hybrid ARIMA-ANN time series models for high frequency data. Methodology: The study made use of ARIMA, ANN, and hybrid ARIMA-ANN models to forecasts the East Africa Community countries' daily currency exchange rates data which were obtained from the Central Bank of Kenya website and covered the period from January 2017 to December 2023. Stationarity of the time series data was established using the ADF test. The Ljung Box test and ACF plots were used to establish and compare the goodness-of-fit of the resultant models while the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error(RMSE) values were used to compare the prediction performance. Results: The study established that the hybrid ARIMA-ANN methodolgy provided better-fitting models for the currency exchange rates data compared to ARIMA and ANN modeling strategies since all the Lyung Box test statistics had p values greater than 5%. Comparatively, the hybrid methodology registered lower MAPE and RMSE values hence had better prediction accuracy compared to ARIMA and ANN methods. Conclusion: The Hybid methodology improves the modeling and forecasting accuracy over the ARIMA and ANN models for high frequency time series data due to its ability to captures both the linear and nonlinear patterns in the time series data.

Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

This study focuses on the parameter estimation of an industrial activated sludge model using hyperparameter-tuned metaheuristic techniques. The data used in this study were collected on-site from a textile industry wastewater treatment plant. A Modified Activated Sludge Model (M-ASM) was the 'first-principle model’ selected and implemented with suitable assumptions. Advanced metaheuristic techniques, as Adaptive Tunicate Swarm Optimization (ATSO), Whale Optimization Algorithm (WOA), Rao-3 Optimization (Rao-3) and Driving Training Based Optimization (DTBO) were implemented. The hyperparameter tuning was performed with Bayesian Optimization (BO). Optimized metaheuristic algorithms were implemented for model-parameter identification. The Bayesian optimized Rao-3(BO-Rao-3) algorithm provided the best validation results, with a Mean Absolute Percentage Error (MAPE) value of 7.0141 and Normalized Root Mean Square Error (NRMSE) value of 0.2629. It also had the least execution time. BO-Rao-3 is 0.93% to 4.7% better than the other implemented hyperparameter-tuned metaheuristic techniques.