Phenological Response of an Evergreen Broadleaf Tree, Quercus acuta, to Meteorological Variability: Evaluation of the Performance of Time Series Models DOI Open Access
Jeong-Soo Park, Hong Ma, Hyohyemi Lee

и другие.

Forests, Год журнала: 2024, Номер 15(12), С. 2216 - 2216

Опубликована: Дек. 16, 2024

Phenological events are key indicators for the assessment of climate change impacts on ecosystems. Most previous studies have focused identifying timing phenological events, such as flowering, leaf-out, leaf-fall, etc. In this study, we explored characteristics green chromatic coordinate (GCC) values evergreen broadleaf tree (Quercus acuta Thunb.), which is a widely used index that serves proxy seasonal and physiological responses trees. Additionally, estimated their relationship with meteorological variables using time series models, including decomposition autoregressive integrated moving average exogenous regressors (SARIMAX). Our results showed GCC variables, were collected at daily intervals, exhibited strong autocorrelation seasonality. This suggests analysis methods more suitable than ordinary least squares (OLS) regression fulfillment statistical assumptions. The highlighted association between precipitation variation in trees, particularly during dry season. These improve our understanding response plant phenology to change.

Язык: Английский

Forecasting Future Groundwater Recharge from Rainfall Under Different Climate Change Scenarios Using Comparative Analysis of Deep Learning and Ensemble Learning Techniques DOI
Dolon Banerjee, Sayantan Ganguly, Shashwat Kushwaha

и другие.

Water Resources Management, Год журнала: 2024, Номер 38(11), С. 4019 - 4037

Опубликована: Апрель 13, 2024

Язык: Английский

Процитировано

11
Navigating the Challenges of Rainfall Variability: Precipitation Forecasting using Coalesce Model DOI Creative Commons
Suraj Kumar Bhagat

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

1
Towards Efficient Electricity Management in Benghazi DOI Creative Commons
Asma Agaal,

Hend M. Farkash,

Mansour Essgaer

и другие.

Solar Energy and Sustainable Development, Год журнала: 2025, Номер 14(FICTS-2024), С. 110 - 136

Опубликована: Янв. 29, 2025

In Libya, the general electricity company is tasked with managing peak demand, often resorting to load shedding. This practice, while necessary, results in power outages, particularly impacting areas like Benghazi Electrical Grid. study aims bring predictability these events by exploring time series forecasting models namely: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Dynamic Regression (DRARIMA). The were trained using data from May 2020 2021, subsequently tested on 2022. Performance was evaluated metrics such as mean squared error, absolute percentage accuracy. model achieved highest accuracy at 78.88% a error of 0.9. SARIMA model, which considers seasonal patterns, an 73.86% 0.11, but its complexity may lead overfitting. DRARIMA, incorporates exogenous variables, demonstrated 65.36% 0.15. Future projections for 2024 2025 indicate potential improvements shedding management highlight importance selection accurate forecasting. By improving accuracy, this research enhance effectiveness management, thereby reducing outages their socio-economic impacts regions Benghazi. These findings are valuable energy planners managers similar contexts, providing practical insights data-driven strategies.

Язык: Английский

Процитировано

0
Rainfall Analysis using FUCOM Weighted Logarithmic Distance Measure Based on Probabilistic Dual Hesitant Preference Values DOI

Rohit Rohit,

Kamal Kumar, Reeta Bhardwaj

и другие.

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 13, 2024

Язык: Английский

Процитировано

2
Time-Series Forecasting of Particulate Organic Carbon on the Sunda Shelf: Comparative Performance of the SARIMA and SARIMAX Models DOI
A’an Johan Wahyudi,

Febty Febriani

Regional Studies in Marine Science, Год журнала: 2024, Номер unknown, С. 103863 - 103863

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

1
Forecasting international tourist arrivals in South Korea: a deep learning approach DOI
Siyu Zhang, Ze Lin, Wii-Joo Yhang

и другие.

Journal of Hospitality and Tourism Technology, Год журнала: 2024, Номер unknown

Опубликована: Авг. 20, 2024

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques diverse sets, the research seeks enhance accuracy reliability of tourism demand predictions, contributing significantly both theoretical implications practical applications in field hospitality tourism. Design/methodology/approach introduces an innovative approach by LSTM networks. advanced methodology addresses complex managerial issues management providing more accurate forecasts. The comprises four key steps: collecting sets; preprocessing data; training network; future arrivals. rest this is structured as follows: subsequent sections detail proposed model, present empirical results discuss findings, conclusions Findings pioneers simultaneous use big encompassing five factors – arrivals, WTI KOSPI cases forecasting. reveals that integrating market enhances network precision. It narrow scope existing on predicting ICN with these factors. Moreover, demonstrates networks’ capability effectively handle multivariable time series prediction problems, basis their application management. Originality/value integration bridges gap literature proposing comprehensive considers simultaneously. Furthermore, it effectiveness networks handling offering insights enhancing predictions. addressing critical techniques, contributes advancement methodologies industry, aiding decision-makers effective planning resource allocation.

Язык: Английский

Процитировано

0
Enhancing the prediction of electric load demand: a comparative analysis of ARIMA and ANN models for the case of a small touristic island DOI Open Access

Cláudio Galli,

Francesco Superchi, Alessandro Bianchini

и другие.

Journal of Physics Conference Series, Год журнала: 2024, Номер 2893(1), С. 012120 - 012120

Опубликована: Ноя. 1, 2024

Abstract Accurate prediction of energy demand is necessary for efficient power system operation, particularly in systems with high renewable sources integration. This study compares different methods predicting load using statistical regression. In particular, the goal to provide insights on differences between simpler AutoRegressive Integrated Moving Average (ARIMA) and more complex Artificial Neural Network (ANN) models. The algorithms are used predict electric Tilos, a small Greek island strong seasonal trends due summer tourism. case significant as Tilos’ outdated electrical grid must adapt an increasing share sources, making forecasting increasingly important. were developed Python open-source tools (such StatsModels TensorFlow). Hyperparameters’ tuning, crucial enhancing effectiveness, was performed stochastic optimization Differential Evolution minimize RMSE. optimal normalized RMSE reported 9.72% ANN 9.54% ARIMA, showing effectiveness both methods, slight edge model. work provides critical information regarding methodologies, highlighting practical guidance managers, policymakers, researchers planning operation.

Язык: Английский

Процитировано

0
Phenological Response of an Evergreen Broadleaf Tree, Quercus acuta, to Meteorological Variability: Evaluation of the Performance of Time Series Models DOI Open Access
Jeong-Soo Park, Hong Ma, Hyohyemi Lee

и другие.

Forests, Год журнала: 2024, Номер 15(12), С. 2216 - 2216

Опубликована: Дек. 16, 2024

Phenological events are key indicators for the assessment of climate change impacts on ecosystems. Most previous studies have focused identifying timing phenological events, such as flowering, leaf-out, leaf-fall, etc. In this study, we explored characteristics green chromatic coordinate (GCC) values evergreen broadleaf tree (Quercus acuta Thunb.), which is a widely used index that serves proxy seasonal and physiological responses trees. Additionally, estimated their relationship with meteorological variables using time series models, including decomposition autoregressive integrated moving average exogenous regressors (SARIMAX). Our results showed GCC variables, were collected at daily intervals, exhibited strong autocorrelation seasonality. This suggests analysis methods more suitable than ordinary least squares (OLS) regression fulfillment statistical assumptions. The highlighted association between precipitation variation in trees, particularly during dry season. These improve our understanding response plant phenology to change.

Язык: Английский

Процитировано

0