Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin

Autor:	Bachir Sakaa, Ahmed Elbeltagi, Samir Boudibi, Hicham Chaffaï, Abu Reza Md. Towfiqul Islam, Luc Cimusa Kulimushi, Pandurang Choudhari, Azzedine Hani, Youssef Brouziyne, Yong Jie Wong
Rok vydání:	2021
Předmět:	Oxygen Support Vector Machine Artificial Intelligence Nitrogen Health Toxicology and Mutagenesis Water Quality Nitrogen Dioxide Environmental Chemistry General Medicine Pollution Water Pollutants Chemical Environmental Monitoring
Zdroj:	Environmental science and pollution research international. 29(32)
ISSN:	1614-7499
Popis:	The water quality index is one of the prominent general indicators to assess and classify surface water quality, which plays a critical role in river water resources practices. This research constructs a hybrid artificial intelligence model namely sequential minimal optimization-support vector machine (SMO-SVM) along with random forest (RF) as a benchmark model for predicting water quality values at the Wadi Saf-Saf river basin in Algeria. The fifteen input water quality datasets such as biochemical oxygen demand (BOD), oxygen saturation (OS), the potential for hydrogen (pH), chemical oxygen demand (COD), chloride (Cl
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2625aabb71176b822391a9b186b55018 https://pubmed.ncbi.nlm.nih.gov/35192167 Zobrazit plný text záznamu Full text from SpringerLink