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10. Sınırlı Eğitim Verileri Durumunda Orman Yangını Duyarlılık Haritalamasında Makine Öğrenimi Performansının Değerlendirilmesi

Author

SHAFAPOURTEHRANY, Mahyat

Subjects
Remote Sensing, Wildfire, Inventory, Susceptibility, Turkey, Geospatial, Uzaktan Algılama, Orman Yangını, Envanter, Duyarlılık, Türkiye, Jeo-uzamsal
Abstract

Wildfire susceptibility mapping can be affected by several factors. One of the most influential factors is inventory data, its extent, format, and reliability. This study aims to evaluate if the Support Vector Machine (SVM) has the capability to detect and map the forest fire susceptible areas under limited training data conditions. To test this hypothesis wildfires in Mugla province located in the Eastern Mediterranean Region of Turkey have been selected as a pilot study area. The wildfire started in Mugla, on 29 July 2021, that considerably affected the residential areas, animals, and vast areas of forests. Fourteen wildfire influential variables have been used in the analysis as independent variables. Accuracy assessment has been implemented using the Area Under the Curve (AUC) technique. Success rate and prediction rates were (91.42%) and (87.69%) respectively. According to the prediction rate, SVM successfully recognized other burnt areas as the most susceptible regions., Orman yangını duyarlılık haritalaması çeşitli faktörlerden etkilenebilir. En etkili faktörlerden biri envanter verileri, kapsamı, biçimi ve güvenilirliğidir. Bu çalışma, Support Vector Machine’in (SVM) sınırlı eğitim verisi koşulları altında orman yangınına duyarlı alanları tespit etme ve haritalama kabiliyetine sahip olup olmadığını değerlendirmeyi amaçlamaktadır. Bu hipotezi test etmek için Türkiye'nin Doğu Akdeniz Bölgesi'nde yer alan Muğla ilindeki orman yangınları pilot çalışma alanı olarak seçilmiştir. Bahsedilen orman yangını Muğla'da 29 Temmuz 2021'de başlamış ve yerleşim alanlarını, hayvanları ve geniş ormanlık alanları önemli ölçüde etkilemiştir. Bağımsız değişkenler olarak on dört orman yangını etkili değişken analizinde kullanılmıştır. Doğruluk değerlendirmesi için Area Under thr Curve (AUC) tekniği kullanılarak uygulama yapılmıştır. Başarı oranı ve tahmin oranları sırasıyla (%91.42) ve (%87.69)’dir. Tahmin oranına göre, SVM diğer yanık alanları en hassas bölgeler olarak başarıyla tanımladı.

Published
2022

12. Improved flood detection and susceptibility modelling using remote sensing and geographic information system

Author

Shafapourtehrany, Mahyat and Shafapourtehrany, Mahyat

Abstract

Natural hazards such as floods, landslides, and land subsidence are destructive events which cause catastrophic damages to both human lives and properties. Accurate and easy to implement prediction models are needed to forecast these hazards and delineate the susceptible areas. Although several methods and techniques have been proposed and examined by researchers to map the flood susceptible areas and to provide flood inventory maps, however optimized approaches for flood susceptibility mapping and modeling could not be encountered in the international literature. In traditional way of flood mapping, multiple field works are generally performed to map and monitor floods which is often time consuming and not economically viable. In the last few decades remote sensing based mapping has become hugely popular among the research fraternity. However, optical remote sensing (RS) data and the available classification schemes may not be appropriate for flood extent mapping. This is mainly attributed due to the severe presence of cloud cover especially during the flood seasons. For that reason, flood modelers and remote sensing scientists has to rely on the use of active remote sensing data such as space-borne radar data for flood area mapping. In this regard, a combination of optical and radar data is highly sought after in flood mapping. In disaster management flood susceptibility mapping is one of the basic steps. There are various types of methods exist in flood susceptibility mapping e.g. traditional based hydrological methods, statistical, probabilistic and data mining based approaches. Traditional hydrological methods are based on linear assumption and require extensive field work. The most popular statistical methods in flood susceptibility assessment are frequency ratio (FR), weights-of-evidence (WoE), and logistic regression (LR). Similarly, the most commonly used data mining approaches in flood susceptibility assessment are artificial neural networks (ANN), fuzzy l

Published
2015

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