An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

IF 2.5 4区 环境科学与生态学 Q3 ENGINEERING, ENVIRONMENTAL Water Environment Research Pub Date : 2024-10-01 DOI:10.1002/wer.11138
Anusha Nanjappachetty, Suvitha Sundar, Nagaraju Vankadari, Tapas Bapu Bathey Ramesh Bapu, Pradeep Shanmugam
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Abstract

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

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利用优化的深度胶囊晶体边缘图神经网络进行高效的水质指数预测和分类。
世界淡水供应主要来自河流,面临着各种经济活动造成的严重污染,这表明河流水质对公众健康、环境可持续性和有效的污染控制至关重要。本研究通过引入一种估算水质指数(WQI)的新方法,满足了对准确可靠的水质监测的迫切需求。所提出的方法将最先进的优化技术与深度胶囊晶体边缘图神经网络相结合,标志着该领域的重大进展。其创新之处在于整合了混合凤头猪成吉思汗鲨优化算法,用于精确选择特征,确保利用最相关的水质(WQ)指标。此外,使用灰雁优化算法对神经网络的权重参数进行微调,也提高了模型的预测准确性。这种双重优化框架极大地改进了水质指数预测,实现了 6.7 的显著均方误差 (MSE) 和 99% 的准确率。这项研究为水质评估提供了一种稳健、高精度的方法,为环境部门主动管理河流水质、预防污染和评估修复工作的成功与否提供了有力的工具。实践点:新方法结合了优化和深度胶囊晶体边缘图来估算水质指数。预处理包括使用先进算法进行数据清理和特征选择。深度胶囊晶体边缘图神经网络可高精度预测 WQI。灰雁优化微调网络参数,实现精确预测。所提出的方法实现了 6.7 的低 MSE 和 99% 的高准确率。
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来源期刊
Water Environment Research
Water Environment Research 环境科学-工程:环境
CiteScore
6.30
自引率
0.00%
发文量
138
审稿时长
11 months
期刊介绍: Published since 1928, Water Environment Research (WER) is an international multidisciplinary water resource management journal for the dissemination of fundamental and applied research in all scientific and technical areas related to water quality and resource recovery. WER''s goal is to foster communication and interdisciplinary research between water sciences and related fields such as environmental toxicology, agriculture, public and occupational health, microbiology, and ecology. In addition to original research articles, short communications, case studies, reviews, and perspectives are encouraged.
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