An Enhanced Interval Type-2 Fuzzy C-Means Algorithm for Fuzzy Time Series Forecasting of Vegetation Dynamics: A Case Study from the Aksu Region, Xinjiang, China

Accurate prediction of the Normalized Difference Vegetation Index (NDVI) is crucial for regional ecological management and precision decision-making. Existing methodologies often rely on smoothed NDVI data as ground truth, overlooking uncertainties inherent in data acquisition and processing. Fuzzy...

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Vydáno v:Land (Basel) Ročník 14; číslo 6; s. 1242
Hlavní autoři: Chen, Yongqi, Liu, Li, Cao, Jinhua, Wang, Kexin, Li, Shengyang, Yin, Yue
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2025
Témata:
Accuracy
Algorithms
Arid zones
Climate change
Clustering
Comparative analysis
Cost analysis
Data acquisition
Data processing
Datasets
Decision making
Desertification
ecological monitoring
Environmental aspects
Environmental management
Environmental restoration
Forecasting
Forecasts and trends
Fuzzy algorithms
Fuzzy logic
Fuzzy sets
Fuzzy systems
fuzzy time series modeling
interval type-2 fuzzy C-means clustering
Land use
Measurement
NDVI
Normalized difference vegetative index
Parameter uncertainty
Precipitation
Prediction models
Remote sensing
Statistical models
Time series
Time-series analysis
Trends
Vegetation
vegetation dynamic
Vegetation dynamics
China
ISSN:2073-445X, 2073-445X
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Abstract Accurate prediction of the Normalized Difference Vegetation Index (NDVI) is crucial for regional ecological management and precision decision-making. Existing methodologies often rely on smoothed NDVI data as ground truth, overlooking uncertainties inherent in data acquisition and processing. Fuzzy time series (FTS) prediction models based on the Fuzzy C-Means (FCM) clustering algorithm address some of these uncertainties by enabling soft partitioning through membership functions. However, the method remains limited by its reliance on expert experience in setting fuzzy parameters, which introduces uncertainty in the definition of fuzzy intervals and negatively affects prediction performance. To overcome these limitations, this study enhances the interval type-2 fuzzy clustering time series (IT2-FCM-FTS) model by developing a pixel-level time series forecasting framework, optimizing fuzzy interval divisions, and extending the model from unidimensional to spatial time series forecasting. Experimental results from 2021 to 2023 demonstrate that the proposed model outperforms both the Autoregressive Integrated Moving Average (ARIMA) and conventional FCM-FTS models, achieving the lowest RMSE (0.0624), MAE (0.0437), and SEM (0.000209) in 2021. Predictive analysis indicates a general ecological improvement in the Aksu region (Xinjiang, China), with persistent growth areas comprising 61.12% of the total and persistent decline areas accounting for 2.6%. In conclusion, this study presents an improved fuzzy model for NDVI time series prediction, providing valuable insights into regional desertification prevention and ecological strategy formulation.
AbstractList Accurate prediction of the Normalized Difference Vegetation Index (NDVI) is crucial for regional ecological management and precision decision-making. Existing methodologies often rely on smoothed NDVI data as ground truth, overlooking uncertainties inherent in data acquisition and processing. Fuzzy time series (FTS) prediction models based on the Fuzzy C-Means (FCM) clustering algorithm address some of these uncertainties by enabling soft partitioning through membership functions. However, the method remains limited by its reliance on expert experience in setting fuzzy parameters, which introduces uncertainty in the definition of fuzzy intervals and negatively affects prediction performance. To overcome these limitations, this study enhances the interval type-2 fuzzy clustering time series (IT2-FCM-FTS) model by developing a pixel-level time series forecasting framework, optimizing fuzzy interval divisions, and extending the model from unidimensional to spatial time series forecasting. Experimental results from 2021 to 2023 demonstrate that the proposed model outperforms both the Autoregressive Integrated Moving Average (ARIMA) and conventional FCM-FTS models, achieving the lowest RMSE (0.0624), MAE (0.0437), and SEM (0.000209) in 2021. Predictive analysis indicates a general ecological improvement in the Aksu region (Xinjiang, China), with persistent growth areas comprising 61.12% of the total and persistent decline areas accounting for 2.6%. In conclusion, this study presents an improved fuzzy model for NDVI time series prediction, providing valuable insights into regional desertification prevention and ecological strategy formulation.
Audience Academic
Author Yin, Yue
Li, Shengyang
Liu, Li
Chen, Yongqi
Cao, Jinhua
Wang, Kexin
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Snippet Accurate prediction of the Normalized Difference Vegetation Index (NDVI) is crucial for regional ecological management and precision decision-making. Existing...
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SubjectTerms Accuracy
Algorithms
Arid zones
Climate change
Clustering
Comparative analysis
Cost analysis
Data acquisition
Data processing
Datasets
Decision making
Desertification
ecological monitoring
Environmental aspects
Environmental management
Environmental restoration
Forecasting
Forecasts and trends
Fuzzy algorithms
Fuzzy logic
Fuzzy sets
Fuzzy systems
fuzzy time series modeling
interval type-2 fuzzy C-means clustering
Land use
Measurement
NDVI
Normalized difference vegetative index
Parameter uncertainty
Precipitation
Prediction models
Remote sensing
Statistical models
Time series
Time-series analysis
Trends
Vegetation
vegetation dynamic
Vegetation dynamics
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Title An Enhanced Interval Type-2 Fuzzy C-Means Algorithm for Fuzzy Time Series Forecasting of Vegetation Dynamics: A Case Study from the Aksu Region, Xinjiang, China
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Volume 14
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