Error Metrics Research Articles

Training neuro-fuzzy using flower pollination algorithm to predict number of COVID-19 cases: situation analysis for twenty countries

Predicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers.

GL-STGCNN: Enhancing Multi-Ship Trajectory Prediction with MPC Correction

In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted trajectories. Through quantitative analysis of different datasets, it was observed that GL-STGCNN outperforms previous prediction models with a 31.8% improvement in the average displacement error metric and a 16.8% improvement in the final displacement error metric. Furthermore, trajectory correction through model predictive control shows a performance boost of 44.5% based on the initial predicted trajectory distribution. While GL-STGCNN excels in multi-ship interaction trajectory prediction by reasonably modeling ship interaction adjacency matrices and employing trajectory correction, its performance may vary in different datasets and ship motion patterns. Future work could focus on adapting the model’s ship interaction adjacency matrix modeling to diverse environmental scenarios for enhanced performance.

Decomposition of full-waveform LiDAR data utilizing an adaptive B-spline-based model and particle swarm optimization

High-precision waveform decomposition is crucial for LiDAR applications. Existing methods encounter challenges including poor target detection and low accuracy in extracting parameters of irregular components, especially in complex echoes. We introduce an adaptive B-spline-based decomposition (AdaptB-spline) method, which uses B-spline curves to adaptively adjust the shape and position of component through the particle swarm optimization (PSO); and proposes an initial parameter estimation method based on the B-spline and Richardson-Lucy (RL) deconvolution, which improves the noise immunity and component detection. Experiments were conducted on synthetic waveforms and satellite LiDAR waveforms by AdaptB-spline and other four methods (Gaussian (Gauss), B-spline-based (B-spline), skew-normal (SkewN), and multi-Gaussian (MultiGauss) decomposition). We concluded that AdaptB-spline exhibits superior performance in terms of component RMSE, CC, R2, component parameter error and range error metrics compared to the four methods. So AdaptB-spline can enhance component detection and accurately fit Gaussian or non-Gaussian waveforms, demonstrating outstanding target detection and ranging precision.

Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting.

Support vector regression-based heteroscedastic models for cryptocurrency risk forecasting

In this study, we aimed to combine support vector regression (SVR) with an autoregressive (AR) model and a generalized autoregressive conditional heteroscedastic (GARCH) or asymmetric GARCH model. We compared the performances of these combined models with the maximum likelihood estimation (MLE)-based models under a normal, Student’s t, or skewed Student’s t distributional assumption. We employed such models to protect the heteroscedastic risks of four major cryptocurrencies, namely, Bitcoin, Ethereum, Tether, and Binance Coin, before and during COVID-19. We accomplished this task by forecasting their volatility, Value-at-Risk (VaR), and Expected Shortfall (ES). The empirical results revealed that in terms of the root-mean-square error (RMSE) and mean-absolute error (MAE) metrics, the SVR-based models with a polynomial or radial basis kernel function, which is nonlinear, resulted in a more accurate volatility forecast compared to the MLE-based models under any distributional assumption. The superiority of the former models was more apparent when applied to Tether exhibiting the most stable volatility. This supports evidence of nonlinear characteristics in the cryptocurrency return and volatility series. A combination between SVR and an asymmetric GARCH model also confirms the presence of an asymmetrically nonlinear relationship between the returns and volatility. In addition, the correct-VaR and Kupiec’s backtesting results showed that the SVR-based models tended to produce the VaR forecasts for the four cryptocurrencies with better accuracy at the 1%, 5%, and 10% significance levels compared to the MLE-based ones. The use of the correct-ES metric also leads to the superior ES forecasting performance of the former models at any level. However, McNeil and Frey’s backtesting results demonstrated their worse ES forecasting performance at the 1% level. As the COVID-19 pandemic progressed, we also found an increase in the Bitcoin and Binance Coin market risks as well as a decrease in the Ethereum and Tether market risks.

USNet: underwater image superpixel segmentation via multi-scale water-net

Underwater images commonly suffer from a variety of quality degradations, such as color casts, low contrast, blurring details, and limited visibility. Existing superpixel segmentation algorithms face challenges in achieving superior performance when directly applied to underwater images with quality degradation. In this paper, to alleviate the limitations of superpixel segmentation when applied to underwater scenes, we propose the first underwater superpixel segmentation network (USNet), specifically designed according to the intrinsic characteristics of underwater images. Considering the quality degradation, we propose a multi-scale water-net module (MWM) aimed at enhancing the quality of underwater images before superpixel segmentation. The degradation-aware attention (DA) mechanism is then created and incorporated into MWM to solve light scattering and absorption, which can decrease object visibility and cause blurred edges. By effectively directing the network to prioritize locations that exhibit a considerable decrease in quality, this method enhances the visibility of those specific areas. Additionally, we extract the deep spatial features using the coordinate attention method. Finally, these features are fused with the shallow spatial information using the dynamic spatiality embedding module to embed comprehensive spatial features. Training and testing were conducted on the SUIM dataset, the underwater change detection dataset, and UIEB dataset. Experimental results show that our method achieves the best scores in terms of achievable segmentation accuracy, undersegmentation error, and boundary recall evaluation metrics compared to other methods. Both quantitative and qualitative evaluations demonstrate that our method can handle complicated underwater scenes and outperform existing state-of-the-art segmentation methods.

Modeling rutting depth on RIOHTrack asphalt pavement using Circle LSTMs

In response to the critical need for accurate pavement rutting prediction in pavement design, this study introduces a novel deep-learning model, Circle LSTM, designed for the estimation of rutting depths in asphalt pavements. Leveraging data from a full-scale field accelerated road testing facility in Tongzhou, Beijing, which spans 2.038 kilometres and features 19 distinct asphalt pavements, the Circle LSTM model utilizes variables such as temperature and load axis for its predictions. Methodologically, the Circle LSTM outperforms traditional machine learning approaches by cycling through different LSTM models to enhance short-term dependence modeling, showcasing its strength in addressing nonlinear problems inherent in road engineering. Evaluation of the model on the test dataset yielded promising results, with error metrics showing an average RMSE of 2.4170, an average MAE of 2.9454, an average MAPE of 2.61%, and an average coefficient of determination (average R2) of 0.8253 for 19 different asphalt pavements. These findings underscore the Circle LSTM model’s potential not only for fast and reliable rutting depth prediction but also for broader applications in multivariate time-series forecasting tasks in road engineering and beyond.

A multiple feature fusion-based intelligent optimization ensemble model for carbon price forecasting

Accurately predicting carbon emissions trading price can assist governments in devising policies that strike a balance between economic development and environmental protection. In recent years, the decomposition integration framework has been widely applied for carbon price prediction. However, due to the inadequacy of feature extraction and lack of emphasis on interval forecasting, constructing a stable and efficient forecasting model for carbon price remains a challenging task. Thus, this study proposes an enhanced decomposition integration model for carbon price prediction that conducts multiple feature fusion based on the potential determinants and establishes a multivariate intelligent optimized ensemble model. Firstly, decomposition and entropy algorithms are employed to decompose and reconstruct the carbon price into several simple subsequences to strike the balance between computing speed and the complexity of the model. Subsequently, considering how external factors may affect the sequence, structured data, and unstructured data are both introduced to construct the multiple forecasting model. An intelligent optimization ensemble model is then developed for predicting the reconstructed components based on a deep learning model with an attention mechanism. Lastly, to quantify uncertainty, interval prediction is incorporated for research and analysis. Empirical analysis is conducted on three carbon finance pilot markets in China and the values of Mean Absolute Percentage Error (MAPE) are 2.047, 1.251, and 0.994 respectively. Furthermore, each module of the proposed model significantly contributes to the results and the developed model outperforms the baseline model across various error metrics.

Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia

A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.

Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI.

We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Network for MRI reconstruction without previous training on datasets. It expands the Deep Image Prior approach with a multidomain, sparsity-enforcing loss function to achieve higher image quality at a faster convergence speed than previously reported methods. Two-dimensional MRI data from the FastMRI dataset with Cartesian undersampling in phase-encoding direction were reconstructed for different acceleration rates for single coil and multicoil data. The performance of our architecture was compared to state-of-the-art Compressed Sensing methods and ConvDecoder, another untrained Neural Network for two-dimensional MRI reconstruction. SCAMPI outperforms these by better reducing undersampling artifacts and yielding lower error metrics in multicoil imaging. In comparison to ConvDecoder, the U-Net architecture combined with an elaborated loss-function allows for much faster convergence at higher image quality. SCAMPI can reconstruct multicoil data without explicit knowledge of coil sensitivity profiles. Moreover, it is a novel tool for reconstructing undersampled single coil k-space data. Our approach avoids overfitting to dataset features, that can occur in Neural Networks trained on databases, because the network parameters are tuned only on the reconstruction data. It allows better results and faster reconstruction than the baseline untrained Neural Network approach.

Advances in Real-Time 3D Reconstruction for Medical Endoscopy.

This contribution is intended to provide researchers with a comprehensive overview of the current state-of-the-art concerning real-time 3D reconstruction methods suitable for medical endoscopy. Over the past decade, there have been various technological advancements in computational power and an increased research effort in many computer vision fields such as autonomous driving, robotics, and unmanned aerial vehicles. Some of these advancements can also be adapted to the field of medical endoscopy while coping with challenges such as featureless surfaces, varying lighting conditions, and deformable structures. To provide a comprehensive overview, a logical division of monocular, binocular, trinocular, and multiocular methods is performed and also active and passive methods are distinguished. Within these categories, we consider both flexible and non-flexible endoscopes to cover the state-of-the-art as fully as possible. The relevant error metrics to compare the publications presented here are discussed, and the choice of when to choose a GPU rather than an FPGA for camera-based 3D reconstruction is debated. We elaborate on the good practice of using datasets and provide a direct comparison of the presented work. It is important to note that in addition to medical publications, publications evaluated on the KITTI and Middlebury datasets are also considered to include related methods that may be suited for medical 3D reconstruction.

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL.

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal-mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model's robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices.

Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules

Immersion cooling is a promising thermal management system for LiBs where the cells are submerged in a thermally conductive coolant, thus improving heat dissipation and prolonging battery life. The present study investigates an aligned arranged 4S4P immersion-cooled LiB module to develop a best-fit machine learning (ML) model that could predict the fault position in the module depending on the inputs from the temperature sensors that are optimized under different operating and fault conditions. The Pearson Correlation Coefficient (PCC) feature selection approach is used to optimize the sensors, while the data is generated from numerical simulations on a 4S4P immersion-cooled LiB module. The model validation through internal experimental trials is performed on a 2S2P battery module. The four ML classification algorithms, which include the K Nearest Neighbors, Random Forest (RF), Extreme Gradient, and Long Short Term Memory (LSTM), are trained on the optimized sensors data and are further tested internally and externally to assess their performance on unseen data within and outside the training range. A 5-fold cross-validation process is also implemented, and following a comprehensive comparison of the predictions based on the accuracies and model elapsed time, the best-fit model is identified. The results conclude that while the LSTM model slightly outweighs the other models with an accuracy of 98.18% for the specific external test cases, the RF model is chosen as the best-fit model with a prediction accuracy of 99.7% based on the error metrics and low training time for the internal testing. The outcomes of this present work contribute to the early identification of battery module failures, enhancing safety and reducing costs.

Generalized machine learning models to predict significant wave height utilizing wind and atmospheric parameters

Significant wave height (SWH) is a key parameter for wave energy extraction, ship navigation, oil and gas extraction, coastal structure construction, etc. Direct measurements of SWH using buoys are expensive and accurate over a limited area while numerical models are computationally expensive, inaccurate, with limited generalizability. Thus, this work focuses on developing generalizable machine learning models for predicting SWH from wind parameters (speed, direction, and gust) and atmospheric parameters (temperature and pressure). Two deep learning models (Artificial Neural Network (ANN) and Self Normalizing Neural Network (SNN) and two gradient boosting tree-based models (XGBoost and LightGBM) have been used in this study. Three different data sets were collected from the National Data Buoy Center: Data Set-1 (DS1): 12 years of data from 47 stations; Data Set-2 (DS2): 14 months of additional data from 6 stations randomly selected from DS1; Data Set-3 (DS3): 13 years data from completely 6 new stations. DS1 was split into training, testing, and validation datasets. Training and hyper-parameter tuning was done on the train and validation dataset, while the performance of the models was evaluated on the DS1 test dataset, DS2, and DS3, employing three error metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-square score (R2). The collected data underwent data cleaning, preprocessing, and exploratory data analysis before modeling. The deep learning models have demonstrated superior fitting capacity to the tree-based models, achieving the lowest MSE (0.047), MAE (0.153), and highest R2 score (0.953) on test data. However, the gradient boosting models demonstrate better generalizing capacity than the deep learning models on DS2 and DS3. This study helps further Sustainable Development Goal 7 (SDG 7) by allowing fast and cheap assessment of wave height for ocean energy site development.

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up.

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.

An Eulerian–Lagrangian method of fundamental solutions for the advection–diffusion equation with time dependent coefficients

This paper investigates the application of the Eulerian–Lagrangian method of fundamental solutions (ELMFS), a truly meshless method that couples the Euler–Lagrange Method (ELM) with the time-dependent method of fundamental solution (MFS). It is applied to solve the transient advection–diffusion equation with transport coefficients exhibiting constant and temporally exponential behaviors. The stability and accuracy of the proposed method were analyzed in terms of condition numbers, location of source points, and error metrics. The stability results for constant coefficients of partial differential equations are presented in a map and compared with well-established stability criteria for explicit in-time mesh methods, such as the Courant–Friedrichs–Lewy and Von Neumann criteria. These comparisons demonstrate that ELMFS has larger stable regions.

Comparing the performance of pediatric weight estimation methods

BackgroundWeight estimation is essential in the care of ill children when a weight cannot be obtained. This is particularly important for children with medical complexity, who are at higher risk for adverse drug events. Our objective was to compare the accuracy of different methods of weight estimation in children and stratify by the presence of medical complexity. MethodsWe performed a retrospective cross-sectional study of children (<18 years) seen in the emergency department (ED) or ambulatory clinic from January 1, 2013 to December 31, 2022 at a tertiary academic pediatric center. We compared the performance of nine age-based formulae and two length-based methods using metrics of mean average error (MAE), root mean square error (RMSE), and agreement within 10% and 20% of measured weight. We additionally evaluated the approaches stratified by body mass index (BMI) and the presence of medical complexity. ResultsOf 361,755 children (median age 8.2 years, IQR 2.5–14.2 years; 51.5% male), 59,283 (16.4%) were seen in the ED. Length was measured or available in 21,330 (36.0%) patients in the ED and 293,410 (97%) patients in clinics. The Broselow tape outperformed all methods, with 50.7% estimates within 10% of measured weight, 80.0% estimates within 20% of measured weight, the lowest MAE (2.5 kg), and lowest RMSE (4.5 kg). The Antevy formula was the most accurate age-based formula, with 49.2% estimates within 10% of measured weight, 80.1% estimates within 20% of measured weight, MAE of 2.8 kg, and RMSE of 4.7 kg. Estimates became less accurate as BMI and estimated weight increased for all methods. Among children with medical complexity (14.1%), the Broselow tape consistently outperformed age-based formulae, with 47.7% estimates within 10% of measured weight, 77.1% estimates within 20% of measured weight, MAE of 2.6 kg, and RMSE of 5.4 kg. The Antevy formula remained the most accurate age-based method among children with medical complexity. ConclusionThe Broselow tape predicted weight most accurately in this large sample of children, including among those with medical complexity. The Antevy formula is the most accurate age-based method for pediatric weight estimation.

Optimizing energy storage plant discrete system dynamics analysis with graph convolutional networks

Addressing the challenges of suboptimal model performance and excessive parameters and operations in the optimization of energy storage power plants utilizing Graph Convolutional Network (GCN), this paper introduces a novel approach – the packet-switched graph convolutional network. Initially, a GCN extreme learning machine is established. Drawing inspiration from this solid foundation, we have innovatively crafted a group exchange graph convolution module. This module leverages group graph convolution techniques to amalgamate unique node feature information, tailored to diverse topology graph matrices based on various groupings. This innovative approach ensures that information flows freely and effectively among distinct groupings. Furthermore, we have designed a cutting-edge timing depth separation convolution module, comprising two innovative components. The first component introduces timing depth separation convolution, revolutionizing the original timing convolution module. The second component, the packet-switching graph convolutional network, revolutionizes the time sequence depth separation convolution process. It achieves this by employing 1 × 1 convolutional layers between different feature fusion packets, enabling seamless information exchange between distinct packets. Experimental results demonstrate the efficacy of the proposed model, with root mean square error (RMSE) metrics and root mean square error (MAE) metrics for single-step prediction reaching 46.08 and 26.22 at 60 min, respectively. In multi-step testing, the proposed model exhibits a 14.71 % reduction in RMSE error at the 15-min scale and a 9.29 % reduction at the 60-min scale compared to the benchmark model. This performance improvement enhances the operational efficiency and reliability of the energy storage plant, particularly under dynamic changes in the time series.

Physics‐informed neural network assisted automated design of power amplifier by user defined specifications

AbstractThis article presents a model that can automatically produce a power amplifier's (PA) design parameters, that is, transmission lines (TLs) dimension, from a dataset of user‐specified design goals like gain, efficiency, linearity, and scattering (S‐) parameters. Based on the applied boundary conditions, a synthetic dataset is generated with the best range of design parameters (W and L). This dataset is utilized for training the physics‐informed neural network (PINN) model with user‐specified design goals as input and design parameters as target to produce the optimum value of W and L as the resultant output. Furthermore, utilizing the obtained dimensions, design, simulation, fabrication, and measurement of a PA are performed to validate our proposed model. The results of large signal measurements of PA are drain efficiency (DE) of 26.9%, power added efficiency (PAE) of 24.7%, output power (Pout) of 30.98 dBm at an input power of 19 dBm, and gain of 12.41 dB at an operating frequency of 1.625 GHz. It has been observed that the design parameters produced by the model have a significant agreement with the validated output. Also, the statistical error analysis is done by calculating the error metrics between the validated output and the actual output of the PA design.

Development of an Integrated Observation Data and Global Numerical Weather Prediction System-Based Rainfall-Runoff Forecasting Model as PJT-I’s Effort in Water Resources Disaster Mitigation

Given the vital role of water resources and their vulnerability to climate-driven disasters, especially floods and droughts, the need for effective disaster mitigation strategies in water resource management is evident. PJT-I, a key player in the industry, grapples with this challenge. To address it, PJT-I has embraced the Smart Water Management System (SWMS) platform and innovative enhancements in rainfall-runoff modeling, showcasing their dedication to bolstering real-time monitoring and predictive capabilities. These advances hold the promise of strengthening operational water resource management, spanning allocation and infrastructure operations, and are particularly relevant in the face of escalating extreme weather events. The study’s focus on the Sengguruh reservoir catchment area serves as an illustration, highlighting the creation of two hydrometric-based models: the AWLR Gadang model and the Sengguruh reservoir model. Their effectiveness is validated through data calibration and integration of GFS forecast data, enabling 120-hour forecasting. Rigorous assessment involving correlation, efficiency, determination, and error metrics provides insights into the models’ predictive capacities and limitations amid the dynamic interplay of rainfall-runoff dynamics.