Ahmad, Ali

Profile Picture
Organizational Units
OrgUnit Logo
Organizational Unit
Job Title
Last Name
Ahmad
First Name
Ali
ORCID
0000-0001-5530-7374
Personal page
Página usuario
Name
Email Address

Filters

20241
Reset filters

Settings

Author: ae8e0881-ea59-43d1-a818-e8cb955f684a ×

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Publication
    Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity
    (MDPI AG, 2024-03) Parra, Lorena; Ahmad, Ali; Sendra, Sandra; Lloret Mauri, Jaime; Lorenz, Pascal; Dpto. de Comunicaciones; Escuela Politécnica Superior de Gandia; Instituto de Investigación para la Gestión Integrada de Zonas Costeras; Generalitat Valenciana; Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación
    [EN] Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.