An IoT Based Water Quality Classification Framework for Aqua-Ponds Through Water and Environmental Variables Using CGTFN Model

IF 2.6 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Peda Gopi Arepalli, K. Jairam Naik, Jagan Amgoth
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引用次数: 0

Abstract

Maintaining water quality in aquatic habitats is critical for the health of aquatic species, particularly fish. This study pioneers an innovative method to water quality classification, leveraging IoT-driven data acquisition and meticulous data labelling with the Aqua-Enviro Index (AEI) by considering the fish habitats. Existing mechanisms fail to capture complex temporal dynamics and depend largely on large amounts of labelled data, exposing fundamental limits. In response, we describe the Deep learning based Convolutional Gated Recurrent Unit Tempo Fusion Network (CGTFN) model, which represents a considerable development in the evaluation of water quality. The model addresses these restrictions by seamlessly merging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Gated Recurrent Units (GRUs) for temporal interactions. The Tempo Fusion mechanism combines spatial, temporal, and contextual data harmoniously, allowing for more sophisticated classifications by recognizing subtle interdependencies among environmental elements. The pioneering CGTFN model outperforms previous models, achieving 99.71 and 99.81% accuracy on both public-env and real-time-env datasets, respectively, exceeding established models at 98.2%. These remarkable findings highlight CGTFN’s disruptive potential in water quality evaluation, bridging the gap between technology and environmental management, with ramifications ranging from aquaculture to resource sustainability.

Abstract Image

利用 CGTFN 模型,通过水和环境变量为水塘建立基于物联网的水质分类框架
保持水生栖息地的水质对水生物种(尤其是鱼类)的健康至关重要。本研究开创了一种创新的水质分类方法,通过考虑鱼类的栖息地,利用物联网驱动的数据采集和水环境指数(AEI)进行细致的数据标注。现有的机制无法捕捉复杂的时间动态,主要依赖于大量的标记数据,这暴露了其根本局限性。为此,我们介绍了基于深度学习的卷积门控递归单元节奏融合网络(CGTFN)模型,该模型代表了水质评价领域的重大发展。该模型通过无缝融合用于空间模式识别的卷积神经网络(CNN)和用于时间交互的门控递归单元(GRU)来解决这些限制。Tempo 融合机制将空间、时间和上下文数据和谐地结合在一起,通过识别环境要素之间微妙的相互依存关系,实现更复杂的分类。开创性的 CGTFN 模型超越了以往的模型,在公共环境和实时环境数据集上分别达到了 99.71% 和 99.81% 的准确率,超过了 98.2% 的既有模型。这些非凡的发现彰显了 CGTFN 在水质评价方面的颠覆性潜力,弥补了技术与环境管理之间的差距,影响范围从水产养殖到资源可持续性。
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来源期刊
CiteScore
5.40
自引率
0.00%
发文量
104
审稿时长
1.7 months
期刊介绍: International Journal of Environmental Research is a multidisciplinary journal concerned with all aspects of environment. In pursuit of these, environmentalist disciplines are invited to contribute their knowledge and experience. International Journal of Environmental Research publishes original research papers, research notes and reviews across the broad field of environment. These include but are not limited to environmental science, environmental engineering, environmental management and planning and environmental design, urban and regional landscape design and natural disaster management. Thus high quality research papers or reviews dealing with any aspect of environment are welcomed. Papers may be theoretical, interpretative or experimental.
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