Water quality polluted by total suspended solids classified within an Artificial Neural Network approach

• URL: http://arxiv.org/abs/2410.14929v1
• Date: Sat, 19 Oct 2024 01:33:08 GMT
• Title: Water quality polluted by total suspended solids classified within an Artificial Neural Network approach
• Authors: I. Luviano Soto, Y. Concha Sánchez, A. Raya,
• Abstract summary: Water pollution by suspended solids poses significant environmental and health risks. To address these challenges, we developed a model that leverages a comprehensive dataset of water quality from total suspended solids. A convolutional neural network was trained under a transfer learning approach using data corresponding to different total suspended solids concentrations.
• Score: 0.0
• License:
• Abstract: This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges, we developed a model that leverages a comprehensive dataset of water quality from total suspended solids. A convolutional neural network was trained under a transfer learning approach using data corresponding to different total suspended solids concentrations, with the goal of accurately predicting low, medium and high pollution levels based on various input variables. Our model demonstrated high predictive accuracy, outperforming conventional statistical methods in terms of both speed and reliability. The results suggest that the artificial neural network framework can serve as an effective tool for real-time monitoring and management of water pollution, facilitating proactive decision-making and policy formulation. This approach not only enhances our understanding of pollution dynamics but also underscores the potential of machine learning techniques in environmental science.

Related papers

• Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations [18.456223143834105]
  This paper introduces a framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen concentrations in lakes. We have tested our methods on a wide range of lakes in the Midwestern USA, and demonstrated robust capability in predicting DO concentrations even with limited training data.
  arXiv  Detail & Related papers  (2024-11-20T01:58:20Z)
• Machine Learning for Urban Air Quality Analytics: A Survey [27.96085346957208]
  Air pollution poses an urgent global concern with far-reaching consequences. In this article, we present a comprehensive survey of Machine Learning-based air quality analytics.
  arXiv  Detail & Related papers  (2023-10-14T17:03:29Z)
• Beyond Tides and Time: Machine Learning Triumph in Water Quality [0.0]
  This study aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge. Our research aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge.
  arXiv  Detail & Related papers  (2023-09-29T03:33:53Z)
• Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms [0.5735035463793009]
  This study proposes a deep learning model that quantifies interpretability, with an emphasis on water level forecasting. We perform a comparative analysis on the Han River dataset obtained from Seoul, South Korea, from 2016 to 2021.
  arXiv  Detail & Related papers  (2023-02-28T04:37:26Z)
• On Robust Numerical Solver for ODE via Self-Attention Mechanism [82.95493796476767]
  We explore training efficient and robust AI-enhanced numerical solvers with a small data size by mitigating intrinsic noise disturbances. We first analyze the ability of the self-attention mechanism to regulate noise in supervised learning and then propose a simple-yet-effective numerical solver, Attr, which introduces an additive self-attention mechanism to the numerical solution of differential equations.
  arXiv  Detail & Related papers  (2023-02-05T01:39:21Z)
• Contaminant source identification in groundwater by means of artificial neural network [0.0]
  The aim of the paper is to develop a data-driven model that is able to analyze multiple scenarios, even strongly non-linear, in order to solve forward and inverse transport problems. The advantages produced by the model were compared with literature studies.
  arXiv  Detail & Related papers  (2022-07-19T14:51:30Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.