使用时间序列卷积神经网络进行水生水数据质量分析的物联网框架
Peda Gopi Arepalli1, Jairam Naik Khetavath2
1Department of Computer Science & Engineering, National Institute of Technology Raipur, Raipur, India.
概括
这项研究介绍了一种基于物联网的深度学习模型,即时间序列卷积神经网络 (TMS-CNN),用于精确监测渔场水质. 新型TMS-CNN模型的准确度达到96.2%,超过了预测鱼类健康状况的现有方法.
科学领域:
- 水产养殖是水产养殖的一种方式.
- 环境监测 环境监测
- 人工智能的人工智能
背景情况:
- 有效的水质监测对于可持续的水产养殖至关重要.
- 传统的水质分析方法在养鱼场中存在重大挑战.
- 需要先进的,自动化解决方案是迫切的.
研究的目的:
- 开发和评估基于物联网 (IoT) 的深度学习模型,用于实时监测渔场水质.
- 与现有方法相比,提高水质分析的准确性和效率.
- 使用时间序列卷积神经网络 (TMS-CNN) 进行时空数据分析.
主要方法:
- 基于物联网的系统与深度学习模型集成,特别是时间序列卷积神经网络 (TMS-CNN).
- 该TMS-CNN模型旨在有效处理时空水质数据,捕获复杂的依赖关系.
- 水质指数 (WQI) 是通过相关性分析计算的,其次是类标签分配和时间序列数据分析.
主要成果:
- 拟议的TMS-CNN模型在分析与鱼类生长和死亡相关的水质参数方面表现出高准确性 (96.2%).
- 该模型的性能通过实现更高的准确率 (91%) 超过了当前最佳模型 (MANN).
- TMS-CNN有效地处理了水质数据中的时空依赖.
结论:
- 基于物联网的TMS-CNN模型为渔场水质监测提供了高度准确和有效的解决方案.
- 这种深度学习方法显著改进了传统方法和现有模型.
- 这些发现支持采用先进的人工智能技术,以优化水产养殖管理和鱼类健康.
相关概念视频
01:19Quality of Water
136
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
136
01:14Testing Water Quality
144
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
144
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
74
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
74
01:24Rapidly Varying Flow
105
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
105
00:54Time-Series Graph
4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
01:29Gradually Varying Flow
91
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
91