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

This study evaluates the performance of traditional statistical methods, specifically the Seasonal Autoregressive Integrated Moving Average (SARIMA), in comparison to advanced deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN)—for wind speed forecasting across various regions in Saudi Arabia: Al-Jouf, Abha, Al-Ahsa, and Al-Dawadami. The historical wind speed dataset covering the year 2018 was utilized, with data preprocessing conducted using the Exploratory Data Analysis (EDA) method to ensure consistency and quality. The models were assessed based on three primary error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). Results show that CNN consistently outperforms SARIMA and the other deep learning models, particularly when forecasting stable wind speed patterns. LSTM demonstrates an ability to handle fluctuating wind speeds effectively, while BiLSTM offers advantages in capturing complex bidirectional temporal dependencies. On the other hand, SARIMA generally exhibits lower performance compared to deep learning approaches. The superior performance of CNN is likely attributed to its strength in local feature extraction and handling spatial patterns effectively, which are beneficial for short-term forecasting. These findings provide valuable insights into model selection for wind energy forecasting and can contribute to optimizing renewable energy integration and planning strategies. Future work may explore hybrid model approaches to further enhance forecasting accuracy.

Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.

Wind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific comunity. This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. In these analyses, five different models were created with the assistance of cross-correlation. The models obtained from the analyses were improved with the wavelet transformation (WT), and the results obtained were evaluated for the correlation coefficient (R), the Nash–Sutcliffe model efficiency (NSE), the Kling–Gupta efficiency (KGE), the performance index (PI), the root mean standard deviation ratio (RSR), and the root mean square error (RMSE). The results obtained from this study unveiled that LSTM emerged as the best performance metric in the M04 model among other models (R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248). Another notable finding obtained from this study was that the best performance values in analyses without WT were obtained with SARIMA. The results of this study provide information on forward-looking modeling for institutions and decision-makers related to energy and climate change.

Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study.Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: Overall, 13670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)12', NNAR (12,1,6)12' , and SARIMA (2,0,2) (1,1,0)12- NNAR (5,1,4)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. Conclusion: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions.

Background: Malaria remains a significant public health problem in India, particularly in endemic states such as Karnataka. Accurate forecasting of malaria incidence is essential for strengthening surveillance systems, optimizing resource allocation and enabling timely preventive interventions. Objective: This study aimed to analyze the temporal pattern of malaria cases in Karnataka State, India and to develop an appropriate Seasonal Autoregressive Integrated Moving Average (SARIMA) model for forecasting future monthly malaria incidence. Methods: A retrospective time series analysis was conducted using monthly malaria case data from January 2020 to August 2025 obtained from the National Centre for Vector Borne Diseases Control (NCVBDC), Government of India. The Box–Jenkins methodology was applied for model development. Data preprocessing included logarithmic transformation and seasonal and non-seasonal differencing to achieve stationary. Model identification was guided by autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. Competing SARIMA models were estimated using maximum likelihood estimation and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC). Residual diagnostics were assessed using the Ljung–Box Q test. Results: The malaria time series demonstrated a clear increasing trend with pronounced seasonal variation. Among the candidate models, the SARIMA (2,1,0) (0,1,1) ₁₂ model showed the best overall performance, with the lowest RMSE (29.413), MAE (18.279), MAPE (40.437) and normalized BIC (7.054). The Ljung–Box Q test indicated no residual autocorrelation (p = 0.77). Twelve-month forecasts (September 2025–August 2026) revealed marked seasonal peaks, with forecasted values ranging from 43 to 461 cases. Conclusion: The SARIMA (2,1,0) (0,1,1) ₁₂ model effectively captured the trend and seasonal dynamics of malaria transmission in Karnataka. SARIMA based forecasting offers a practical and reliable tool for malaria surveillance and can support evidence-based planning and timely public health interventions. Keywords: Malaria, Time series analysis, SARIMA, Forecasting, Karnataka, Public health

Satellite observations of the ocean surface provide a powerful method for acquiring global data on wind speed and wave height. Radar altimeters have now been in operation for more than 25 years, providing a reasonably long term data set with global coverage. This paper presents data from a fully calibrated and validated altimeter dataset. The dataset provides the basis for obtaining a global perspective of a number of parameters critical to ocean engineering design, ship operations and global climate change. Analysis of the data provides ocean climatology of mean monthly values of wind speed and wave height useful for ship operations. The data set is also sufficiently long to provide extreme value (i.e. 100-year return period) estimates of wind speed and wave height. The paper presents such values and describes the approaches most appropriate to obtain statistically significant extreme value estimates from such satellite data. With a data set of this length, it is possible to investigate whether there have been statistically significant changes in the wind and wave climates over the period. Careful trend analysis of the extensive data set shows that there has been a statistically significant increasing trend in mean wind speed over the period. The corresponding increase in wave height is less clear. There is also evidence to suggest that extreme wind speeds and wave heights are increasing and the data set is analysed to investigate these trends. The paper clearly shows the value of this dataset and its application to a range of engineering problems.

The purpose of this study is to predict the decade rainfall in Semarang City using two main methods, namely Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Long Short Term Memory (LSTM). The methodology of this study begins with data pre-processing, which includes data deletion analysis using dropna and data normalization using Min-Max Scaling to reduce scale the to between 0 and 1. The dataset is then divided into 80% training data and 20% test data. The validity of the data (X_test, Y_test) using the best 56-epoch data validation (val_loss) is better than the validity of the training data (loss). On the other hand, SARIMAX uses the (2,1,2), (2,1,2,36) model, and its validation techniques include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Specifically, the RMSE of the LSTM model is 19.6, and its RMSE of the SARIMAX is 31.05. The MAE of LSTM is 15.0, SARIMAX is 24.5, and the R2 of LSTM is 0.814, and SARIMAX is 0.52. Lower RMSE and MAE values indicate lower prediction errors, but a higher R2 value of 1 indicates that LSTM can explain 81% of the actual data variation, which is better than SARIMAX, which is only about 52%. The main finding of this study is that the LSTM model performs better when recommending rainfall datasets.

This study conducts a comprehensive empirical analysis of modeling and forecasting the daily stock prices of PZ Cussons Nigeria PLC using the Seasonal Autoregressive Integrated Moving Average (SARIMA) framework within the Box-Jenkins methodology. Utilizing a dataset of 2,289 daily closing prices spanning from January 5, 2015, to November 22, 2023, the research rigorously compares the forecasting performance of SARIMA against a non-seasonal ARIMA benchmark. Stationarity is assessed via the Augmented Dickey-Fuller (ADF) test, with model selection guided by the Akaike Information Criterion (AIC). The optimal models identified are SARIMA(1,0,1)(0,3,2,20) and ARIMA(5,3,0), with diagnostic checks confirming adequacy through residual analysis and Ljung-Box tests. Forecast accuracy is evaluated out-of-sample using root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) across multiple horizons. Results indicate that while ARIMA exhibits marginally lower RMSE in short-term forecasts, SARIMA consistently outperforms in MAPE and demonstrates superior robustness over medium to long-term horizons, particularly beyond one month. Trading strategy backtests further reveal that SARIMA-based signals yield significantly higher cumulative returns (15.5% vs. 8.1%), hit rates (56% vs. 47%), and Sharpe ratios (1.12 vs. 0.68), with lower maximum drawdowns. These findings underscore the critical role of seasonality potentially driven by institutional rebalancing, fiscal cycles, and behavioral market responses in Nigerian equity price dynamics. The study contributes to the literature on financial econometrics in emerging African markets by providing empirical evidence that SARIMA offers enhanced predictive accuracy and practical trading utility compared to traditional ARIMA, affirming the relevance of classical time series models in contemporary quantitative finance.

Background: This study was designed to find the best statistical approach to scorpion sting predictions.Study Design: A retrospective study.Methods: Multiple regression, seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly scorpion sting cases in El Oued province. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to quantitatively compare different models.Results: In general, 96909 scorpion stings were recorded in El Oued province from 2005-2020. The incidence rate experienced a gradual decrease until 2012 and since then slight fluctuations have been noted. Scorpion stings occurred throughout the year with peaks in September followed by July and August and troughs in December and January. Sting cases were not evenly distributed across demographic groups; the most affected age group was 15-49 years, and males were more likely to be stung. Of the reported deaths, more than half were in children 15 and younger. Scorpion’s activity was conditioned by climate factors, and temperature had the highest effect. The SARIMA(2,0,2)(1,1,1)12, NNAR(1,1,2)12, and SARIMA(2,0,2)(1,1,1)12-NNAR(1,1,2)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were lower than those of the NNAR model in fitting and forecasting; however, the NNAR model could produce better predictive accuracy.Conclusion: The NNAR model is preferred for short-term monthly scorpion sting predictions. An in-depth understanding of the epidemiologic triad of scorpionism and the development of predictive models ought to establish enlightened, informed, better-targeted, and more effective policies.

BackgroundGonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.MethodsTime series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models.ResultsThe Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE.ConclusionsThe overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA)

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time–series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008–2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

Offshore wind turbines (OWTs), in comparison to onshore wind turbines, are gaining popularity worldwide since they create a large amount of electrical power and have thus become more financially viable in recent years. However, OWTs are costly as they are vulnerable to damage from extremely high-speed winds and thereby affect operation and maintenance (O&M) operations (e.g., vessel access, repair, and downtime). Therefore, accurate weather forecasting helps to optimise wind farm O&M operations, improve safety, and reduce the risk for wind farm operators. Sequential data-driven models recently found application in solving the wind turbines problem; however, their application to improve offshore operation and maintenance through weather forecasting is still limited and needs further investigation. This paper fills this gap by proposing three sequential data-driven techniques, namely, long short-term memory (LSTM), bidirectional LSTM (BiLSTM) and gated recurrent units (GRU) for long-term weather forecasting. The proposed techniques are then compared to summarise the strength and weaknesses of these models concerning long-term weather forecasting. Weather datasets (wind speed and wave height) are intermittent over different time scales and reflect offshore weather conditions. These datasets (obtained from the FINO3 database) will be used in this study for training and validation purposes. The study results suggest that the proposed technique can generate more realistic and reliable weather forecasts in the long term. It can also be stated that it responds better to seasonality and forecasted expected results. This is further validated by the calculated values of statistical performance metrics and uncertainty quantification.

Ocean wind waves follow shifts in atmospheric circulation and wind regimes caused by climate change and variability. We use a wave model (WAM) forced by winds from an Earth system model (EC‐Earth) to study the evolution of global wave climate in terms of trends in wind speed and wave height, their rates and anomalies over the 250 years. Historical period and future scenarios (RCP8.5 and RCP4.5) are calculated within the fifth phase of the Coupled Model Intercomparison Project. Both scenarios indicate a future increase of wind speed and wave height in the Arctic and Southern Ocean and a decrease in the Pacific Ocean. Some regions, e.g., the North Atlantic show a change of sign from currently positive to negative trends in this century. Swell dominated regions, connected to the Southern Ocean show opposite trends for winds and waves. Calculated trends are evaluated taking into account natural variability estimated from a simulation with pre–industrial climate. We conclude that projected wave heights are still within natural variability, whereas wind speed exceeds it in the Arctic and Southern Ocean. Probability estimates show that extreme winds and waves become more dominant in the Southern Hemisphere, Arctic and Indian Ocean. Low and medium winds and waves are highly probable in the North and Equatorial Atlantic and Pacific.

Cow-calf operations form the foundation of the U.S. beef industry, with approximately 27.9 million beef cows and 25.1 million calves born in 2024, comprising 61.1% of total cattle inventory. Though their exact economic contribution is hard to predict due to the integrated nature of the beef supply chain, the cattle industry contributed approximately $88.4 billion (17%) to the total U.S. agricultural receipts in 2023. Despite their significance, these operations face economic challenges, including fluctuating feed costs, market volatility, and drought. Given the narrow profit margins of these operations, predictive modeling of profitability can offer valuable insights for sustainable management and decision-making. This study applies three univariate (Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), SARIMA with Exogenous regressors (SARIMAX)), three multivariate machine learning models (Random Forest (RF), AdaBoost, Support Vector Regression (SVR)), and one deep learning model (Long Short-Term Memory, LSTM) to assess the economic performance of cow-calf operations, specifically predicting the average farm price received for calves ($/Cwt). The analysis leverages historical data along with key domain-specific variables, including feed costs (monthly average U.S. farm price of corn, $/bushel), fuel prices (industrial natural gas price, $/thousand cubic feet), foreign exchange rates (U.S.-Japan exchange rate), and inflation indices (Consumer Price Index, CPI), to provide robust and enhanced predictive insights. A cow-calf budget dataset spanning from 1996 to 2024 was compiled from sources including the USDA Agricultural Marketing Service and Texas A&M AgriLife Extension. Data acquisition was facilitated through web scraping techniques, enabling the automated extraction of the historical economic indicators. The collected data underwent preprocessing and feature engineering, to enhance model input quality, and both univariate time-series and multivariate machine learning models were applied to capture temporal and nonlinear patterns in calf prices. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Mean Percentage Error (MPE), Root Mean Square Error (RMSE), Root Mean Square Percentage Error (RMSPE), and Coefficient of Determination (R²) to comprehensively assess predictive performance. The analysis revealed substantial relationships between selling price of cow-calf operations and key economic indicators. Among the evaluated forecasting models, SARIMA exhibited the strongest performance by effectively capturing seasonal trends, while SARIMAX showed comparable accuracy through the integration of external variables. Among the machine learning techniques, AdaBoost performed best, outperforming RF and SVR. The LSTM model achieved the overall best performance, demonstrating strong capability in modeling both nonlinear dependencies and temporal dynamics in calf price trends. These findings demonstrate the potential of combining classical time-series and modern machine learning models to support price forecasting in cow-calf operations. This hybrid modeling approach provides a powerful decision-support tool for producers navigating market volatility, and future research should explore real-time forecasting and broader economic and environmental integrations to further strengthen predictive capabilities.