Using Decision Tree and K-Means to Improve ANFIS for Predicting Missing Flow Data in Çoruh Basin
This research introduces an advanced ANFIS model enhanced by Decision Tree (DT) and K-Means (KM) methods for superior prediction of missing streamflow data in Türkiye's Çoruh Basin. The integrated DT-K-Means-ANFIS model significantly outperforms traditional ANFIS, Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) models, achieving a remarkable R² of 0.98 and a low WMSE of 5.89 during testing. This innovation streamlines input variable selection and membership function determination, drastically reducing model development time and boosting prediction accuracy for critical water resource management.
Executive Impact Metrics
0.98
Prediction Accuracy (R²)
5.89
Weighted Mean Square Error (WMSE)
75%
Model Development Time Reduction
Deep Analysis & Enterprise Applications
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The study introduces a novel hybrid modeling approach by integrating Decision Tree (DT) and K-Means clustering with the Adaptive Neuro-Fuzzy Inference System (ANFIS). This integration addresses the common challenges in ANFIS modeling, such as the manual selection of input variables and the arbitrary determination of membership function numbers. By using DT for input variable selection and K-Means for optimizing membership functions, the model significantly enhances prediction accuracy and efficiency. Traditional ANN and MLR models are also implemented for comparative performance analysis, showcasing the superiority of the hybrid approach.
Decision Trees (DT), specifically the CHAID algorithm, were employed to identify the most influential input variables for predicting missing flow data. This systematic approach eliminated the need for extensive trial-and-error, ensuring that only statistically significant stations were included in the ANFIS model. The DT analysis revealed that stations 2305, 2316, and 2338 were crucial for accurate predictions for station 2335, streamlining the model complexity and improving interpretability.
K-Means clustering was utilized to determine the optimal number of membership functions for the ANFIS model. By grouping data points into distinct clusters, the algorithm provided a data-driven method to define the fuzzy sets, which are critical components of ANFIS. This step is vital for avoiding overfitting and ensuring that the model captures the underlying patterns in the data effectively, leading to more robust and reliable predictions.
The hybrid DT-K-Means-ANFIS model demonstrated superior performance with an R² value of 0.98 and a WMSE of 5.89 during the testing phase. This significantly outperformed standalone ANFIS models, ANN, and MLR, which showed lower R² values and higher WMSE, indicating greater prediction errors. The Wilcoxon test further confirmed the statistical significance of the hybrid model's improved accuracy, highlighting its robustness and reliability for practical applications.
0.98
Achieved R² in Testing Phase
Enterprise Process Flow
Raw Flow Data Acquisition (Çoruh Basin)
→
Decision Tree (DT) for Input Selection
→
K-Means for Optimal Membership Functions
→
ANFIS Model Generation
→
Missing Flow Data Prediction for Station 2335
→
Water Resource Management Insights
| Model | Key Advantages | Limitations |
|---|---|---|
| DT-K-Means-ANFIS |
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| Standard ANFIS |
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| ANN |
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| MLR |
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Impact on Çoruh Basin Water Management
The Çoruh Basin in Türkiye frequently faces challenges with missing streamflow data due to various factors including environmental conditions and equipment malfunctions. The DT-K-Means-ANFIS model provides a reliable and systematic solution for reconstructing these missing records. This enhanced data completeness is crucial for informed decision-making in flood analysis, drought assessment, and optimized water allocation planning. By accelerating the model-building process and improving accuracy, the proposed method directly supports more resilient and efficient water resource management strategies in critical regions.
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Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Implementation Timeline & Roadmap
A phased approach to integrating this advanced AI methodology into your enterprise operations.
Phase 1: Data Preparation & DT Analysis
Collection and preprocessing of historical flow data; application of Decision Tree to identify optimal input stations (2 weeks).
Phase 2: K-Means Optimization
Clustering of selected input data using K-Means to determine the ideal number of membership functions for ANFIS (1 week).
Phase 3: ANFIS Model Development & Training
Construction and training of the hybrid DT-K-Means-ANFIS model using 75% of the data (3 weeks).
Phase 4: Validation & Performance Evaluation
Rigorous testing of the model with the remaining 25% of the data, comparing performance against ANN and MLR (2 weeks).
Phase 5: Integration & Deployment
Integration of the validated model into existing water resource management systems for continuous missing data prediction (2 weeks).
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