Development of Multiple Regression Models and Multiple Linear Percepteron Model For Prediction of Heavy Metal Concentration in Sediment

Year 2019, Special Issue 2019, 389 - 397, 31.10.2019

İsmail İşeri , Sema Arıman

https://doi.org/10.31590/ejosat.638354

Cited By: 1

Abstract

In recent years, metal pollution in the aquatic environment has attracted global attention due to its high availability, persistence and environmental toxicity. The behavior of metals in natural water is a function of substrate sediment composition, suspended sediment composition and water chemistry. The sediment is an inseparable and dynamic part of the river basin which includes various habitats and environments. In assessing the quality of sediments, the determination of anthropogenic inputs of heavy metals in aquatic ecosystems due to their long half-life is considered important and may reflect the history of pollution in aquatic ecosystems. Therefore, it is very important to determine and estimate the concentration of heavy metals that affect sediment quality. In this study, it has been realized that heavy metal concentration in mid-Black Sea coastal sea and river sediments can be estimated by using multivariate linear regression (MLR), multivariate polynomial regression (MPR) and multiple layer percepteron (MLP) models. Physico-chemical parameters of sediment samples taken from 5 different points between 2007-2008 for testing and training of models pH, water content (WC), cation exchange capacity (CEC), oxidation reduction potential (ORP), electrical conductivity (Ec), zeta potential (ζP), total carbon (TC), total inorganic carbon (TIC) and total organic carbon (TOC) and heavy metals (Cu, Cr, Cd, Pb, Ni, Fe, Al, Sr, Mn and Cr
In this study, the performance comparisons of MLR, MPR and MLP models were performed to estimate each heavy metal concentration. As a result, physico-chemical parameters were considered as independent variables in the prediction of concentration of heavy metals in sediments, and regression analyzes were performed and it was found that the best results were obtained with MPR model.

Keywords

Prediction, Multivariate Regression, Artifical Neural Network, Heavy Metal, Sediment

Sedimandaki Ağır Metal Konsantrasyonunun Çoklu Değişken Regresyon Modelleri ve Çok Katmanlı Algılayıcı Ağ Modeli ile Tahmini

Abstract

Son yıllarda,
su ortamındaki metal kirliliğinin fazla bulunması, kalıcılığı ve çevresel
toksisitesi nedeniyle küresel olarak dikkat çekmektedir. Metallerin doğal
sudaki davranışı, substrat sediment bileşiminin, askıda sediment bileşiminin ve
su kimyasının bir işlevidir. Sediman, çeşitli habitatlar ve ortamlar içeren
nehir havzasının ayrılmaz ve dinamik bir parçasıdır. Sedimanların kalitesinin
değerlendirilmesinde, uzun yarılanma süreleri nedeniyle sucul ekosistemlerdeki
ağır metallerin, antropojenik girdilerinin belirlenmesi önemli olarak kabul
edilir ve sucul ekosistemlerdeki kirlilik geçmişini yansıtabilir. Bu nedenle,
sediman kalitesini etkileyen ağır metallerin konsantrasyonunun belirlenmesi ve
tahmin edilmesi oldukça önem taşımaktadır. Bu çalışma kapsamında Orta Karedeniz
Kıyı şeridi deniz ve ırmak sedimanlarında ağır metal konsantrasyonunun çoklu
değişkenli lineer regresyon (MLR), çoklu değişkenli polinomal regresyon (MPR) ve
Çok Katmanlı Algılayıcı Ağ (MLP) modelleri kullanılarak tahminlenebilmesi gerçekleştirilmiştir.
Modellerin test ve eğitimleri için 2007-2008 yılları arasında 5 farklı noktadan
alınan sediman örneklerine ait fiziko-kimyasal parametreler pH, su içeriği
(WC), katyon değişim kapasitesi (CEC), oksidasyon redüksiyon potansiyeli (ORP),
elektriksel iletkenlik (EC), zeta potansiyeli (ζP), toplam karbon (TC), toplam
inorganik karbon (TIK), toplam organik karbon (TOK) ve ağır metallerin (Cu, Cr,
Cd, Pb, Ni, Fe, Al, Sr, Mn ve Cr) konsantrasyonları kullanılmıştır. Çalışmada
her bir ağır metal konstantrasyonu tahmin edilmesi için MLR, MPR ve MLP modelllerinin
performans karşılaştırlmaları yapılmıştır. Sonuç olarak sedimanlardaki ağır
metallerin konsantrasyonun tahminlemesinde fiziko-kimyasal parametreler
bağımsız değişkenler olarak kabul edilerek, regresyon analizleri yapılmış ve gerçekleştirilen
modeler arasında en iyi sonucun MPR modeli ile elde edildiği ortaya
konulmuştur. 

Keywords

Tahminleme, Çoklu Regresyon, Yapay Sinir Ağları, Ağır Metal, Sediman

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