Artificial intelligence-based prediction models for environmental engineering

Title:

Artificial intelligence-based prediction models for environmental engineering

Creator:

Yetilmezsoy , Kaan, Ozkaya , Bestamin , and Cakmakci , Mehmet

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:4f97f1cd-5e69-4978-9692-8fc1433b8a21
uuid:4f97f1cd-5e69-4978-9692-8fc1433b8a21
doi:10.14311/NNW.2011.21.012

Subject:

Enviromental engineering, artificial neural networks, adaptive neuro -uzzy inference system, and black-box modeling

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A literature survey was conducted to appraise the recent applications of artifical intelligence (AI)-based modeling studies in the environmental engineering field. A number of studies on artificial neural networks (ANN), fuzzy logic and adaptive neuro-fuzzy systems (ANFIS) were reviewed and important aspects of these models were highlighted. The results of the extensive literature survey showed that most AI-based prediction models were implemented for the solution of water/wastewater (55.7%) and air pollution (30.8%) related environmental problems compared to solid waste (13.5%) management studies. The present literature review indicated that among the many types of ANNs, the three-layer feed-forward and back-propagation (FFBP) networks were considered as one of the simplest and the most widely used network type. In general, the Levenberg-Marquardt algorithm (LMA) was found as the best-suited training algorithm for several complex and nonlinear real-life problems of environmental engineering. The literature survey showed that for water and wastewater treatment processes, most of AI-based prediction models were introduced to estimate the performance of various biological and chemical treatment processes, and to control effluent pollutant loads and flowrates from a specific system. In air polution related environmental problems, forecasting of ozone (O3) and nitrogen dioxide (NO2) levels, daily and/or hourly particulate matter (PM2.5) and PM10) emissions, and sulfur dioxide (SO2) and carbon monoxide (CO) concentrations were found to be widely modeled. For solid waste management applications, reseachers conducted studies to model weight of waste generation, solid waste composition, and total rate of waste generation.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2011 Volume:21 | Number:3

Harvested from:

CDK

Metadata only:

false