Key Takeaways & Executive Findings
- •• • Established a power-law relationship between transparency and turbidity (y = 3.12x−0.66), setting a critical turbidity threshold of 46.9 NTU for blackening and odor, enabling turbidity as a surrogate for transparency in monitoring. • • Identified five core indicators (turbidity, DO, TP, CODMn, NH3-N) via ANOVA, RFE, and RF, with turbidity as the most important, providing a parsimonious yet comprehensive set for predicting blackening and odor. • • The VIKOR-based composite index model achieved high predictive accuracy (RMSE = 0.029, MAE = 0.020) and consistency (NSE = 0.918, R2 = 0.918), outperforming other indices (R2 < 0.84), ensuring reliable early warning. • • The model exhibited robust performance across multiple river basins (Yangtze, Pearl, Haihe, Yellow), indicating its generalizability for nationwide application in urban water management.
Abstract
The construction of predictive models for the recurrence of blackening and odor in urban water bodies has become a critical foundation for refined management of urban water environments. Based on water quality monitoring data from 16 cities in the Yangtze River Basin from 2020 to 2023, this study systematically evaluated six typical comprehensive index calculation methods and proposed a probabilistic prediction model for water blackening and odor recurrence centered on the VIKOR composite index. Through sampling analysis and literature review, a power-law relationship between transparency (y) and turbidity (x) was established (y = 3.12x−0.66), leading to a critical turbidity threshold of 46.9 NTU for blackening and odor. Using ANOVA, recursive feature elimination, and random forest, five indicators—turbidity, dissolved oxygen (DO), total phosphorus (TP), permanganate index (CODMn), and ammonia nitrogen (NH3-N)—were selected as the model's indicator system, with importance ranking: turbidity > DO > TP > CODMn > NH3-N. The VIKOR composite index exhibited the most robust mapping relationship with blackening probability, achieving high accuracy (RMSE = 0.029, MAE = 0.020) and consistency (NSE = 0.918, R2 = 0.918), whereas models based on other indices yielded R2 values below 0.84. The model demonstrated good predictive performance across the Yangtze, Pearl, Haihe, and Yellow River basins. This model offers a universal decision-making tool for precise identification, early warning, and targeted management of water blackening and odor recurrence, with potential integration into urban water smart platforms.
1. Introduction
Urban water bodies worldwide face the recurring challenge of blackening and odor, undermining remediation efforts and posing risks to public health and aquatic ecosystems. Existing prediction models, primarily mechanistic or purely data-driven, often fail to capture the complex, multi-factor interactions driving these phenomena. Mechanistic models require extensive parameterization and are computationally intensive, while conventional data-driven approaches may overlook the directional (positive or negative) influences of key water quality indicators. This study addresses this bottleneck by integrating a multi-criteria decision-making method, VIKOR, which effectively distinguishes beneficial and non-beneficial factors, into a composite index for probabilistic prediction.
Leveraging a large dataset from the Yangtze River Basin (420 million records, subsampled to 77,500 after quality control), we systematically compared six composite index methods and employed rigorous feature selection (ANOVA, RFE, RF) to identify the most predictive indicators. The resulting VIKOR-based model demonstrates superior accuracy and consistency, offering a robust, transferable tool for early warning and targeted management of water blackening and odor recurrence. This approach not only advances predictive modeling but also provides a practical framework for integrating such models into smart water management platforms.
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ZHANG Qi, ZHANG Jie, WANG Song, ZHOU Zhen, TANG Rui (2026). Construction of a VIKOR Composite Index-Based Probabilistic Prediction Model for Urban Water Blackening and Odor Recurrence Using Multi-Method Feature Selection. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202507062
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Frequently Asked Questions
What is the physical basis for using turbidity as a surrogate for transparency in the blackening and odor assessment?
The study established a power-law relationship between transparency (y) and turbidity (x): y = 3.12x−0.66, based on field samples from two rivers in Shanghai. This relationship allows the conversion of turbidity measurements to transparency equivalents, with a critical turbidity threshold of 46.9 NTU corresponding to the transparency criterion of 25 cm. This surrogate is essential because national monitoring systems often lack transparency data but provide turbidity data.
How were the five core indicators selected, and what is their relative importance?
Three feature selection methods were employed: ANOVA (filter), recursive feature elimination with SVM (wrapper), and random forest (embedded). All three consistently identified turbidity, DO, TP, CODMn, and NH3-N as the most relevant indicators. The importance ranking, derived from random forest Gini impurity reduction, was turbidity > DO > TP > CODMn > NH3-N, indicating turbidity as the dominant driver.
What are the performance metrics of the VIKOR-based model compared to other composite indices?
The VIKOR-based model achieved RMSE = 0.029, MAE = 0.020, NSE = 0.918, and R2 = 0.918. In contrast, models based on other indices (geometric mean, CRITIC, factor analysis, grey relational, TOPSIS) all had R2 values below 0.84, demonstrating the superior predictive accuracy and consistency of the VIKOR approach.
How was the model validated across different river basins, and what does this imply for its generalizability?
The model was tested on data from the Yangtze, Pearl, Haihe, and Yellow River basins, showing good predictive performance in all cases. This cross-basin validation suggests that the model captures universal mechanisms of blackening and odor, making it suitable for nationwide application in urban water management.
What are the practical implications of this model for urban water management?
The model provides a quantitative tool for early warning of blackening and odor recurrence, enabling proactive management. With high accuracy and consistency, it can be integrated into smart water platforms to guide targeted interventions, potentially reducing the social and economic costs associated with water quality deterioration.
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