Rani J Priskilla Angel, Rubavathi C Yesubai
Department of Computer Science and Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India.
Department of Computer Science and Engineering, Saveetha Engineering College, Thandalam, Tamil Nadu, India.
PeerJ Comput Sci. 2024 Nov 29;10:e2488. doi: 10.7717/peerj-cs.2488. eCollection 2024.
AquaMeld, a novel method for reusing agricultural and industrial wastewater in rivers and lakes, is presented in this article. Water shortage and environmental sustainability are major problems, making wastewater treatment a responsibility. Customizing solutions for varied stakeholders and environmental conditions using standard methods is challenging. This study uses AquaMeld and Multi-Layer Perceptron with Recurrent Neural Network (MLP-RNN) algorithms to create a complete recommendation system. AquaMeld uses MLP-RNN to evaluate complicated wastewater, environmental, and pH data. AquaMeld analyses real-time data to recommend wastewater reuse systems. This design can adapt to changing scenarios and user demands, helping ideas grow. This technique does not assume data follows a distribution, which may reduce the model's predictive effectiveness. Instead, it forecasts aquatic quality using RNN-MLP. The main motivation is combining the two models into the MLP-RNN to improve prediction accuracy. RNN handles sequential data better, whereas MLP handles complex nonlinear relationships better. MLP-RNN projections are the most accurate. This shows how effectively the model handles complicated, time- and place-dependent water quality data. If other environmental data analysis projects have similar limits, MLP-RNN may work. AquaMeld has several benefits over traditional methods. The MLP-RNN architecture uses deep learning to assess complicated aquatic ecosystem interactions, enabling more proactive and accurate decision-making is the most accurate with a 98% success rate. AquaMeld is flexible and eco-friendly since it may be used for many agricultural and industrial operations. AquaMeld helps stakeholders make better, faster water resource management choices. Models and field studies in agricultural and industrial contexts examine AquaMeld's efficacy. This strategy enhances environmental sustainability, resource exploitation, and wastewater reuse over previous ones. According to the results, AquaMeld might transform wastewater treatment. River and lake-dependent companies and agriculture may now use water resource management methods that are less destructive.
本文介绍了AquaMeld，一种在河流和湖泊中再利用农业和工业废水的新方法。水资源短缺和环境可持续性是主要问题，这使得废水处理成为一项责任。使用标准方法为不同的利益相关者和环境条件定制解决方案具有挑战性。本研究使用AquaMeld和带有递归神经网络的多层感知器（MLP-RNN）算法创建了一个完整的推荐系统。AquaMeld使用MLP-RNN来评估复杂的废水、环境和pH数据。AquaMeld分析实时数据以推荐废水再利用系统。这种设计可以适应不断变化的场景和用户需求，有助于想法的发展。该技术不假设数据遵循某种分布，这可能会降低模型的预测有效性。相反，它使用RNN-MLP来预测水质。主要动机是将这两种模型结合到MLP-RNN中以提高预测准确性。RNN能更好地处理序列数据，而MLP能更好地处理复杂的非线性关系。MLP-RNN的预测最为准确。这表明该模型能有效地处理复杂的、依赖时间和地点的水质数据。如果其他环境数据分析项目有类似的局限性，MLP-RNN可能会发挥作用。与传统方法相比，AquaMeld有几个优点。MLP-RNN架构使用深度学习来评估复杂的水生生态系统相互作用，能够实现更主动、准确的决策，成功率最高可达98%。AquaMeld具有灵活性且环保，因为它可用于许多农业和工业操作。AquaMeld有助于利益相关者做出更好、更快的水资源管理选择。在农业和工业背景下的模型和实地研究检验了AquaMeld的有效性。与之前的方法相比，这种策略增强了环境可持续性、资源开发和废水再利用。根据结果，AquaMeld可能会改变废水处理方式。依赖河流和湖泊的公司和农业现在可以采用破坏性较小的水资源管理方法。
