Veri Bağımlı Yaklaşımlarla Coğrafi Çevresel Faktörler Dikkate Alınarak Buğday Tarımı için Alan Kullanım Uygunluğunun Değerlendirmesi

Year 2021, , 528 - 542, 15.09.2021

Onur Şatır , Süha Berberoğlu

https://doi.org/10.29133/yyutbd.898307

Cited By: 2

Abstract

Temel çevresel faktörler dikkate alınarak, standart sapma tabanlı çok ölçütlü değerlendirme (ÇÖD) ve yapay sinir ağları (YSA) olarak iki teknik, Van Bölgesi’ndeki buğday tarımına uygun alanların belirlenmesi için araştırılmıştır. İklim verileri (uzun dönemli en yüksek, en düşük, ortalama sıcaklıklar, toplam yağış ve güneş radyasyonu), yükseklik, tepe gölgelikleri (güneşlenme), eğim, toprak derinliği, alansal erişilebilirlik ve arazi örtüsü ve kullanımı verileri buğday tarımı için uygun alanların belirlenmesinde kullanılmıştır. Bütün kullanılan girdi verileri, sezonluk buğday tarımı yapılan alanlar temel alınarak ağırlıklandırılmıştır. ÇÖD ve YSA aynı girdi verileri kullanılarak performans değerledirmesi için karşılaştırılmıştır. Toplamda 228 buğday parselinin 171’i eğitim verisi ve 57 parseli ise test verisi olarak kullanılmıştır. Göreceli çalışma karakteristiği (GÇK), doğruluk analizi için uygulanmıştır. ÇÖD ve YSA tekniklerinin GÇK doğruluk katsayıları sırasıyla; 0.875 ve 0.71 olarak elde edilmiştir. Çalışma sonuçlarına göre; Van İli arazilerinin % 15’inin buğday tarımına çok uygun olduğu ancak bu alanların % 67’sinin tarım için kullanıldığı, geri kalan alanların % 28’inin çayır-mera, % 4’ünün açık alan ve % 1’inin ise diğer alanlardan oluştuğu belirlenmiştir.

Keywords

Tarımsal alan kullanım uygunluğu, Buğday, Çok ölçütlü değerlendirme, Yapay sinir ağları, CBS

Thanks

Van tarım İl Müdürlüğü'ne buğday ekim alanları verileriyle ilgili desteklerinden dolayı teşekkür ederiz.

Evaluation of Land Use Suitability for Wheat Cultivation Considering Geo-Environmental Factors by Data Dependent Approaches

Abstract

Two techniques were investigated to be standard deviation based weighting Multi Criteria Assessment (MCA), and Artificial Neural Network (ANN) considering base environmental factors to define wheat cultivation suitability in Van region. Climate data (long term annual, maximum and minimum temperature, total mean precipitation and solar radiation), physical factors such as elevation, hillshade, slope, soil depth, accessibility to the fields and land use cover were used to produce wheat suitability map. All inputs were weighted with reference to existing wheat areas. MCA and ANN approaches were applied using same dataset to compare the performance of the two techniques. In total, 228 wheat parcels were used as training (171 parcels) and testing (57 parcels) data. Relative Operational Characteristic (ROC) was applied for accuracy assessment. ROC values of the MCA technique which was depended on existing wheat lands, and ANN techniques were derived to be 0.875 and 0.71 respectively. Results showed that 15% of the research area was very suitable for wheat farm, and today, only 67% of very suitable areas were used to be agriculture. Other areas were currently used as grassland (28%), bare ground (4%), and other (1%).

Keywords

Agricultural land use suitability, Wheat, Multi-criteria assessment, Artificial neural network, GIS

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