Forecast of Water Structure Based on GM (1, 1) of the Gray System

Dong, Yazhou; Ren, Zheng; Li, Lian Hui

doi:10.1155/2022/8583959

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Currently, the methods for predicting water quality characteristics can be categorized into three groups. The first type includes traditional statistical methods of prediction, such as the common gray system theory model [7] and the Markov model [8]. Ghaemi et al [9] developed an autoregressive integrated moving average model (ARIMA) for predicting water quality characteristics in the distribution network using static and dynamic samples to improve the performance of the water distribution network and warn of contamination within the network.…”

Section: Introductionmentioning

confidence: 99%

A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA

Zuo,

Gou,

Wang

et al. 2023

Water

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With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model’s root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction.

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Section: Introductionmentioning

confidence: 99%