Global Potato Production Forecasting Based on Time Series Analysis and Advanced Waterwheel Plant Optimization Algorithm

The cultivation of potatoes is one of the most important parts of the world’s agricultural system, so forecasting methods that can precisely predict the direction of production are needed. We focus on the area of optimization techniques herein in this study and develop a particular use of metaheuris...

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Bibliographic Details
Published in:Potato research Vol. 67; no. 4; pp. 1965 - 2000
Main Authors: Alhussan, Amel Ali, Khafaga, Doaa Sami, Abotaleb, Mostafa, Mishra, Pradeep, El-Kenawy, El-Sayed M.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.12.2024
Springer
Subjects:
Agriculture
Algorithms
Analysis
Biomedical and Life Sciences
data collection
Forecasts and trends
Life Sciences
Machine learning
Mathematical optimization
Plant Genetics and Genomics
Plant Sciences
Plant Systematics/Taxonomy/Biogeography
Potatoes
system optimization
time series analysis
Vegetable industry
Waterwheel Plant Algorithm
Feature selection
Statistical analysis
Metaheuristic search
ARIMA
Optimization
ISSN:0014-3065, 1871-4528
Online Access:Get full text
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Summary:The cultivation of potatoes is one of the most important parts of the world’s agricultural system, so forecasting methods that can precisely predict the direction of production are needed. We focus on the area of optimization techniques herein in this study and develop a particular use of metaheuristic algorithms applied to improve predictive models. Among such algorithms, the Waterwheel Plant Algorithm (WWPA) is notable for its efficiency in enhancing the autoregressive integrated moving average (ARIMA) model. Feature selection, an essential preprocessing step in machine learning, is of the highest significance in our approach. We apply the bWWPA method to select the most central features from the dataset, which, in turn, improves the whole predictive model’s performance. Through the identification of the main patterns and links in the data, feature selection allows for the model to focus on the most influential factors, giving way to more precise predictions. The WWPA-ARIMA model obtained by our method captures the essential features after optimization and thus involves a very low root mean square error (RMSE) of 0.0001. Such a high level of precision emphasizes the efficiency of our optimization procedure in adjusting the ARIMA model parameters carefully to reveal the hard-to-catch patterns in potato production data. To evaluate the robustness of our method, we employ strong statistical analyses, such as ANOVA and the Wilcoxon signed-rank test. This test also gives additional evidence that our optimization method works better than alternative approaches.
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ISSN:0014-3065
1871-4528
DOI:10.1007/s11540-024-09728-x