İçeriğe geç
akaturk Akademik ölçüm

Makale detayı · 2011

ARTIFICIAL INTELLIGENCE-BASED PREDICTION MODELS FOR ENVIRONMENTAL ENGINEERING

Dergi

Neural Network World
OpenAlex SJR Q3 JCR Q4 Atıf 129 Yüzdelik 87.7% FWCI 1.92
Yıl
2011
Tür
article

Veri kaynağı ayrımı

  • YÖKSİS dergi adı Neural Network World
  • OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex · İngilizce

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment.As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks.Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions.In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection.For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used.As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms.A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

129 atıf

OpenAlex cited_by_count (önbellek / veritabanı)

Yazarlar

Yazar bilgisi yok.