Prediction of Uptake of Carbamazepine and Diclofenac in Reclaimed Water-Irrigated Lettuces by Machine Learning Techniques

  1. Raquel Martínez-España
  2. Andrés Bueno-Crespo
  3. Mariano González García
  4. Carmen Fernández-López
Libro:
Agriculture and environment perspectives in intelligent systems
  1. Andrés Muñoz Ortega (ed. lit.)
  2. Jaehwa Park (ed. lit.)

Editorial: IOS Press

ISBN: 978-1-61499-968-3

Año de publicación: 2019

Páginas: 72-90

Tipo: Capítulo de Libro

DOI: 10.3233/AISE190005 WoS: WOS:000637753600005 DIALNET GOOGLE SCHOLAR

Objetivos de desarrollo sostenible

Resumen

Currently, due to the global shortage of water, the use of reclaimed water from the Wastewater Treatment Plants (WWTPs) for the irrigation of crops is an alternative in areas with water scarcity. However, the use of this reclaimed water for vegetable irrigation is a potential entry of pharmaceutical products into the food chain due to the absorption and accumulation of these contaminants in different parts of the plants. In this work we carried out an analysis of five machine learning techniques (Random Forest, support vector machine, M5 Rules, Gaussian Process and artificial neural network) to predict the uptake of carbamazepine and diclofenac in reclaimed water-irrigated lettuces with the consequent saving of environmental and economic costs. For the different combinations of input and output, the prediction results using the of machine learning techniques proposed on the pharmaceutical components in reclaimed water-irrigated lettuces are satisfactory, being the best technique the Random Forest that obtains a model fit value (R-2) higher than 96.5% using a single input in the model and higher than 97% using two inputs in the model.