Sinir Ağı Kullanarak Düzenli Depolama Alanı Uygunluk Haritalaması için Entegre MATLAB-ArcGIS Araç Kutusu

Abstract

Atık depolama Sahası Uygunluk Haritalaması için Sinir Ağı Araç Kutusu, Python-ArcGIS ve MATLAB ortamlarının bir kombinasyonunu kullanarak depolama sahası seçimini otomatikleştirerek katı atık yönetimini geliştirmek için yeni bir yaklaşım sunar. Bu çalışma, çevresel risklerin en aza indirilmesi ve arazi kullanımının optimize edilmesi için gerekli olan verimli depolama sahası uygunluk haritalamasına yönelik kritik ihtiyacı ele almaktadır. Karar vericiler için kullanıcı dostu bir iş akışını kolaylaştıran, MATLAB’ın sinir ağı modelleme yeteneklerini ArcGIS’teki Python komut dosyası oluşturma ile birleştiren otomatik bir araç kutusu geliştirdik. Metodolojimiz Malezya Yarımadası’nın kuzey bölgesinde, eğitim ve test veri kümeleri oluşturmak için 14 spesifik kriter kullanılarak test edildi. Sinir ağı modelinin sonuçları %82 doğruluk oranına ulaşmıştır. Bu araç kutusu, veri hazırlamadan uygunluk haritalamasına kadar çeşitli aşamaları otomatikleştirerek, insan hatası olasılığını azaltarak ve işlem süresini en aza indirerek süreci kolaylaştırır. Araç kutusunun esnekliği, birlikte çalışabilirliği ve kullanıcı dostu arayüzü, onu farklı düzeylerde CBS uzmanlığına sahip kullanıcılar için erişilebilir hale getirerek farklı coğrafi bölgelere ve çevre koşullarına uyarlanabilirliğini sağlar. Bu araştırma, daha verimli, doğru ve sürdürülebilir depolama alanı seçimine katkıda bulunarak karar vericilere fayda sağlar ve daha geniş çevre koruma çabalarını destekler.

Keywords

• Atık Depolama Sahasına Uygunluk Haritalaması
• Sinir Ağı
• ArcGIS
• Karar Vericiler
• Mekansal Veriler

An Integrated MATLAB-ArcGIS Toolbox for Landfill Suitability Mapping Using Neural Networks

Abstract

The Neural Network Toolbox for Landfill Suitability Mapping presents a novel approach to enhance solid waste management by automating landfill site selection using a combination of Python-ArcGIS and MATLAB environments. This study addresses the critical need for efficient landfill suitability mapping, which is essential for minimizing environmental risks and optimizing land use. We developed an automated toolbox that integrates the capabilities of MATLAB for neural network modeling with Python scripting in ArcGIS, facilitating a user-friendly workflow for decision-makers. Our methodology was tested in the northern region of Peninsular Malaysia, employing 14 specific criteria to generate training and testing datasets. The results of neural network model achieving an accuracy of 82%. This toolbox streamlines the process by automating various stages, from data preparation to suitability mapping, reducing the likelihood of human errors and minimizing processing time. The flexibility, interoperability, and user-friendly interface of the toolbox make it accessible to users with varying levels of GIS expertise, ensuring its adaptability to different geographic regions and environmental conditions. This research contributes to more efficient, accurate, and sustainable landfill site selection, benefiting decision-makers and supporting broader environmental protection efforts.

Keywords

• Landfill Suitability Mapping
• Neural Networks
• ArcGIS
• Decision-Makers
• Spatial Data

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