Ilgaz Dağı Milli Parkı Doğal Çam Orman Arazilerinin Çölleşme Risk Değerlendirmesinde Bulanık-AHP Yaklaşımı ve Yapay Zekâ Kullanımı

Abstract

Bu çalışmanın amacı, çam ormanlarıyla kaplı alanların çölleşme risk değerlendirmesinde Akdeniz Avrupası için Çölleşme Gösterge Sistemi (DIS4ME) yaklaşımında ele alınan indikatörleri dikkate alarak, Ilgaz Dağı Milli Park sınırları içerisinde doğal çam orman arazilerinin çölleşme risk değerlendirmesini yapmaktır. Çölleşme risk değerlendirmesinde 8 indikatör (yağış, kuraklık, toprak bünyesi, taşlılık, bitki örtüsü-kapalılık, eğim, derinlik ve bakı) ele alınmıştır. Fakat, DIS4ME yaklaşımından farklı olarak bu çalışmada ele alınan indikatör indeks değerleri modelden birebir alınmayıp, Bulanık-Analitik Hiyerarşik Süreç (Bulanık-AHP) yaklaşımı ile daha da hassaslaştırılmıştır. Toprakların temel fiziko-kimyasal özellerini belirlemek amacıyla alandan 151 toprak örneği alınmıştır. Ilgaz Dağı Milli Park alanı içerisinde çölleşme risk değerlendirmesi yönünden alanda dağılım gösteren toprakların büyük bir çoğunluğunun çölleşme riski altında olduğu belirlenmiştir. Ayrıca, çalışmada elde edilen model sonuçları yapay sinir ağları ile tahmin edilmiştir. Elde edilen sonuçlara göre, çölleşme riskinin belirlenmesinde % 99 doğrulukla tahmin edilebilir olduğu belirlenmiştir.

Keywords

• Çölleşme
• bulanık-AHS
• yapay zeka
• Ilgaz Dağı Mili Parkı

Fuzzy-AHP Approach and Artificial Intelligence Use in The Desertification Risk Assessment of Natural Pine Forest Lands of Ilgaz Mountain National Park in Türkiye

Abstract

The aim of this study is to conduct a desertification risk assessment of natural pine forest lands within the boundaries of Ilgaz Mountain National Park, taking into account the indicators considered in the Desertification Indicator System for Mediterranean Europe (DIS4ME) approach to the desertification risk assessment of areas covered with pine forests. Eight indicators (rainfall, drought, soil texture, stoniness, vegetation cover, slope, depth and aspect) were considered in the desertification risk assessment. However, unlike the DIS4ME approach, the indicator index values considered in this study were not taken directly from the model and were more refined with the Fuzzy-Analytic Hierarchical Process (Fuzzy-AHP) approach. To determine the basic physico-chemical properties of the soils, 151 soil samples were taken from the study area. In terms of desertification risk assessment of soils within the Ilgaz Mountain National Park area, it has been determined that most of the area is under desertification risk. In addition, the model results obtained in the study were estimated with artificial neural networks (ANN). According to the results obtained, it has been determined that the risk of desertification can be estimated with 99% accuracy in determining.

Keywords

• Desertification
• fuzzy-AHP
• artificial intelligence
• Ilgaz Mountain National Park

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