Article
Environmental Sciences
Xueyuan Bai, Zhenhai Li, Wei Li, Yu Zhao, Meixuan Li, Hongyan Chen, Shaochong Wei, Yuanmao Jiang, Guijun Yang, Xicun Zhu
Summary: This study investigated an effective method for predicting apple fruit yields using time-series remote sensing data. The RF Sigma NDVI model based on Planet images showed better performance in yield prediction accuracy compared to the CASA(SR) model.
Article
Environmental Sciences
Fangjie Li, Jianqiang Ren, Shangrong Wu, Hongwei Zhao, Ningdan Zhang
Summary: This study focused on the winter wheat mapping method based on remote sensing technology, proposing a new method to improve the accuracy of crop area extraction, achieving a high consistency with statistical data.
Article
Agronomy
Rogerio de S. Noia Junior, Luc Olivier, Daniel Wallach, Esther Mullens, Clyde W. Fraisse, Senthold Asseng
Summary: The study aimed to develop a simple methodology to estimate national wheat yields that can be applied to any country and crop by correlating climate data with national wheat production. Statistical models were built using the climate data from the most representative grid cell to estimate trend-corrected national wheat yields, and the models were validated in Brazil, France, and Russia with a small margin of error. This approach allows for early predictions of national crop yields during a cropping season.
EUROPEAN JOURNAL OF AGRONOMY
(2023)
Article
Computer Science, Information Systems
Uppala Meena Sirisha, Manjula C. Belavagi, Girija Attigeri
Summary: Time series forecasting using historical data is crucial in various fields. In this study, ARIMA, SARIMA, and LSTM models were used to analyze profits and make predictions. The results showed that the LSTM model outperformed the statistical models in terms of accuracy.
Article
Agronomy
Maximilian Zachow, Rogerio de S. Noia Jr, Senthold Asseng
Summary: This study improved a statistical wheat yield model to forecast trend-corrected wheat yield in Brazil using seasonal climate models. The results showed that the approach had high accuracy in early-season yield forecasting and improved as the season progressed. The use of seasonal climate models can provide decision support for farmers, food traders, and policymakers to prepare for possible food shortages.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Public, Environmental & Occupational Health
Andrew C. Miller, Lauren A. Hannah, Joseph Futoma, Nicholas J. Foti, Emily B. Fox, Alexander D'Amour, Mark Sandler, Rif A. Saurous, Joseph A. Lewnard
Summary: Accurate measurement of daily infection incidence is crucial to epidemic response. This study introduces the robust incidence deconvolution estimator, which incorporates a regularization scheme to address stochastic delays. Comparison with existing estimators in simulation and real data analysis demonstrates its accuracy and stability.
Article
Biochemistry & Molecular Biology
Mir Henglin, Brian L. Claggett, Joseph Antonelli, Mona Alotaibi, Gino Alberto Magalang, Jeramie D. Watrous, Kim A. Lagerborg, Gavin Ovsak, Gabriel Musso, Olga V. Demler, Ramachandran S. Vasan, Martin G. Larson, Mohit Jain, Susan Cheng
Summary: This study compares the performance of traditional and newer statistical learning methods in different types of metabolomics data and finds that, in the analysis of human metabolomics data, multivariate methods perform better than univariate methods as the number of study subjects increases.
Article
Engineering, Multidisciplinary
W. A. Shaikh, S. F. Shah, S. M. Pandhiani, M. A. Solangi
Summary: This investigative study focuses on the impact of wavelet on traditional forecasting time-series models, and finds that combining wavelet algorithms with traditional models can improve the accuracy of the forecasts.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2022)
Article
Agronomy
Attila Nagy, Andrea Szabo, Odunayo David Adeniyi, Janos Tamas
Summary: Due to the increasing global demand for food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing-based forecast models developed from vegetation indices have the potential to provide quantitative and timely information on crops for larger regions. In this study, wheat yield was derived and validated using Landsat 8-derived NDVI and SAVI-based forecasting models, with SAVI providing more accurate forecasts compared to NDVI.
Article
Environmental Sciences
Xuan Wang, Wenchong Tian, Zhenliang Liao
Summary: This study compared the performance of ARIMA and ANN models in predicting surface water quality, finding that ANN models had significantly lower median validation MSEs and more concentrated distributions, indicating lighter overfitting and better generalization ability. Compared to previous comparisons among selected models, the statistical comparison in this study showed lower uncertainty.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Environmental Sciences
Jining Yan, Haixu He, Lizhe Wang, Hao Zhang, Dong Liang, Junqiang Zhang
Summary: This study provides a benchmark dataset, CUG-FFireMCD1, for forest fire disturbance detection and validates four commonly used time series change detection models. The BFAST model performs the best in detecting forest fire disturbances from MOD13A2 time series. The results also suggest that the CCDC and LandTrendR models can be used for data support in labeling work. However, some model adaptation is recommended for perfect application in MOD13A2 time series change detection.
Article
Computer Science, Information Systems
Federico Gatta, Fabio Giampaolo, Edoardo Prezioso, Gang Mei, Salvatore Cuomo, Francesco Piccialli
Summary: Time series is a widely-used methodology to describe phenomena in various fields. Neural network generative approaches, which aim to generate new samples based on real data by learning the underlying probability distribution, are gaining more relevance in the data analysis community. This paper contributes to the debate by comparing four neural network-based generative approaches for time series, evaluating their performances under different experimental conditions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Review
Environmental Sciences
Jatinder Kaur, Kulwinder Singh Parmar, Sarbjit Singh
Summary: Globalization, industrialization, and urbanization have resulted in economic growth but have negatively impacted the environment. Understanding the detrimental effects on the environment and human health and implementing control measures is crucial. Time series analysis, particularly using the ARIMA model, can help in this direction due to its precision and flexibility. This study reviews the evolution of ARIMA and its applications in various fields, with a special focus on the environment, health, and air quality. It concludes that combined models or hybrid modeling with ARIMA are more robust and effective in capturing patterns in the series uniformly.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Forestry
Anna Kozuch, Dominika Cywicka, Krzysztof Adamowicz
Summary: The majority of timber price forecasting studies have relied on ARIMA/SARIMA models, while VAR and ETS models have been used less frequently. ANN methodology has not been employed for forecasting timber prices in primary timber markets. This study compares RBF and MLP artificial neural networks with the Prophet procedure and classical models (ARIMA, ETS, BATS, and TBATS) for timber price forecasting in Poland. MLP outperforms other models in terms of price change and level forecasting. ANN models better fit minimum and maximum values compared to classical models. The Prophet procedure yields the lowest quality projections.
Article
Computer Science, Information Systems
Abdullah H. Al-Nefaie, Theyazn H. H. Aldhyani
Summary: This study investigates the application of artificial intelligence models to predict the stock prices of a specific company and different sectors on the Saudi Stock Exchange. The results show that the LSTM model performs the most accurately and has excellent model fitting ability.
Article
Chemistry, Multidisciplinary
Charles Cernay, David Makowski, Elise Pelzer
ENVIRONMENTAL CHEMISTRY LETTERS
(2018)
Article
Environmental Sciences
Laure Hossard, Laurence Guichard, Celine Pelosi, David Makowski
SCIENCE OF THE TOTAL ENVIRONMENT
(2017)
Article
Environmental Sciences
Sabine-Karen Lammoglia, David Makowski, Julien Moeys, Eric Justes, Enrique Barriuso, Laure Mamy
SCIENCE OF THE TOTAL ENVIRONMENT
(2017)
Article
Ecology
L. E. Vogel, V. Pot, D. Makowski, P. Garnier, P. C. Baveye
ECOLOGICAL MODELLING
(2018)
Article
Biodiversity Conservation
Peng Zhu, Zhenong Jin, Qianlai Zhuang, Philippe Ciais, Carl Bernacchi, Xuhui Wang, David Makowski, David Lobell
GLOBAL CHANGE BIOLOGY
(2018)
Article
Environmental Sciences
Maharavo Marie Julie Ramanantenasoa, Jean-Marc Gilliot, Catherine Mignolet, Carole Bedos, Etienne Mathias, Thomas Eglin, David Makowski, Sophie Genermont
SCIENCE OF THE TOTAL ENVIRONMENT
(2018)
Article
Multidisciplinary Sciences
Tamara Ben-Ari, Julien Boe, Philippe Ciais, Remi Lecerf, Marijn Van der Velde, David Makowski
NATURE COMMUNICATIONS
(2018)
Article
Multidisciplinary Sciences
M. El Akkari, O. Rechauchere, A. Bispo, B. Gabrielle, D. Makowski
SCIENTIFIC REPORTS
(2018)
Article
Agriculture, Multidisciplinary
Landy Andriamampianina, Ludovic Temple, Hubert de Bon, Eric Malezieux, David Makowski
CAHIERS AGRICULTURES
(2018)
Review
Environmental Sciences
Pierre Martin, Claire Bladier, Bette Meek, Olivier Bruyere, Eve Feinblatt, Mathilde Touvier, Laurence Watier, David Makowski
ENVIRONMENTAL HEALTH PERSPECTIVES
(2018)
Article
Multidisciplinary Sciences
Wei Li, Philippe Ciais, David Makowski, Shushi Peng
Article
Multidisciplinary Sciences
Bernhard Schauberger, Tamara Ben-Ari, David Makowski, Tomomichi Kato, Hiromi Kato, Philippe Ciais
SCIENTIFIC REPORTS
(2018)
Article
Environmental Sciences
Maharavo Marie Julie A. Ramanantenasoa, Sophie Genermont, Jean-Marc Gilliot, Carole Bedos, David Makowski
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2019)
Article
Food Science & Technology
Claude Bragard, Katharina Dehnen-Schmutz, Francesco Di Serio, Paolo Gonthier, Marie-Agnes Jacques, Josep Anton Jaques Miret, Annemarie Fejer Justesen, Alan MacLeod, Christer Sven Magnusson, Panagiotis Milonas, Juan A. Navas-Cortes, Roel Potting, Philippe Lucien Reignault, Hans-Hermann Thulke, Wopke Van der Werf, Antonio Vicent Civera, Jonathan Yuen, Lucia Zappala, David Makowski, Alice Delbianco, Andrea Maiorano, Irene Munoz Guajardo, Giuseppe Stancanelli, Michela Guzzo, Stephen Parnell
Article
Agriculture, Dairy & Animal Science
G. Cantalaiedra-Hijar, R. J. Dewhurst, L. Cheng, A. R. J. Cabrita, A. J. M. Fonseca, P. Noziere, D. Makowski, H. Fouillet, I Ortigues-Marty