Litter categorization of beaches in wales, UK by multi-layer neural networks

Date

2006-12-01

Author

BALAS, CAN ELMAR
Williams, A. T.
Ergin, Ayşen
Koc, M. L.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

66
views

0
downloads

Litter categories and grades of Welsh beaches were satisfactorily predicted by multi-layered feed for-ward neural networks and fuzzy systems, which are artificial intelligence techniques. Neural network structures with hidden layers consisting of 40 neurons of uni-bipolar sigmoid functions were constructed for Welsh beaches and they were trained by supervised (conjugate gradient) learning algorithm to predict the number of litter items and categories from data obtained by 157 litter surveys carried out for 49 beaches in Wales, UK (including the most attractive tourist beaches of Tresaith, Aberporth, Port Eynon, Trecco Bay, Sandy Bay, Swansea Bay, Rest Bay, Lavernock, Goodwick, Amroth Castle, Rhyl Prom and Porthdafarch). The input data for trained neural networks were litter items in general litter category, and the network could predict items in remaining seven categories by learning the relation among them and considering main litter sources in UK (river, shipping, fishing, beach users and sewage related debris). These high-speed predictions saved on field efforts as fast and reliable estimations of litter categories were required for management studies of these beaches. Fuzzy systems were also used to incorporate additional information inherent in linguistic comments/judgments made during field studies and questionnaires distributed to beach users. The artificial intelligence model (ARIM) presented is a universal one to predict litter categories in different countries, which have various litter sources and beach user characteristics.

Subject Keywords

Artificial intelligence, Fuzzy systems, Beach grading

URI

https://hdl.handle.net/11511/53035

Journal

JOURNAL OF COASTAL RESEARCH

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Interacting fuzzy multimodel intelligent tracking system for swift target manoeuvres Gokkus, L; Erkmen, Aydan Müşerref; Tekinalp, Ozan (1997-09-11) This paper focuses on the generation of an intelligent tracker module equipped with a wavelet based neural network that learns predictions from past experience. The perception of actual tar et manoeuvre and prediction of its future states are achieved in this work by "projecting" actual observations into decision spaces of local fuzzy predictions based on independent prototypical trajectory types: linear, parabolic and square root type trajectory. Decentralized tracking decisions are thus generated which ar...
Intuitionistic, 2-way adaptive fuzzy control Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (1999-05-15) Our objective in this paper is to develop a 2-way adaptive fuzzy control system that makes use of the intuitionistic fuzzy sets for modeling expert knowledge bearing uncertainty. Adaptive fuzzy control systems are fuzzy logic systems whose rule parameters are automatically adjusted through training. The training of such system was applied until now, to supports of rule propositions with single distribution such that they can be termed 1-way adaptive. In our system, all supports to propositions have interval...
FUZZY PREDICTION STRATEGIES FOR GENE-ENVIRONMENT NETWORKS - FUZZY REGRESSION ANALYSIS FOR TWO-MODAL REGULATORY SYSTEMS Kropat, Erik; Ozmen, Ayse; Weber, Gerhard Wilhelm; Meyer-Nieberg, Silja; DEFTERLİ, ÖZLEM (2016-04-01) Target-environment networks provide a conceptual framework for the analysis and prediction of complex regulatory systems such as genetic networks, eco-finance networks or sensor-target assignments. These evolving networks consist of two major groups of entities that are interacting by unknown relationships. The structure and dynamics of the hidden regulatory system have to be revealed from uncertain measurement data. In this paper, the concept of fuzzy target-environment networks is introduced and various f...
Nuclear Fission-Nuclear Fusion algorithm for global optimization: a modified Big Bang-Big Crunch algorithm YALÇIN, YAĞIZER; Pekcan, Onur (Springer Science and Business Media LLC, 2020-04-01) This study introduces a derivative of the well-known optimization algorithm, Big Bang-Big Crunch (BB-BC), named Nuclear Fission-Nuclear Fusion-based BB-BC, simply referred to as N2F. Broadly preferred in the engineering optimization community, BB-BC provides accurate solutions with reasonably fast convergence rates for many engineering problems. Regardless, the algorithm often suffers from stagnation issues. More specifically, for some problems, BB-BC either converges prematurely or exploits the promising r...
Evaluating the convergence of high-performance computing with big data, artificial intelligence and cloud computing technologies Dildar Korkmaz, Yeşim; Eren, Pekin Erhan; Kayabay, Kerem; Department of Information Systems (2023-1-24) The advancements in High-Performance Computing (HPC), Big Data, Artificial Intelligence (AI), and Cloud Computing technologies have led to a convergence of these fields, resulting in the emergence of significant improvements for a wide range of fields. Identifying the state of development of technology convergence and forecasting promising technology convergence is critical for both academia and industry. That's why technology assessment and forecasting for HPC-Big Data-AI-Cloud Computing convergence is nee...

Citation Formats

C. E. BALAS, A. T. Williams, A. Ergin, and M. L. Koc, “Litter categorization of beaches in wales, UK by multi-layer neural networks,” JOURNAL OF COASTAL RESEARCH, pp. 1515–1519, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53035.