Developing a forest fire prediction model based on deep learning and forecast a fire risk map of Turkey

Abstract

Forest fires are one of the main problems that cause economic losses by threatening natural life and biological ecosystem by disrupting forestry activities. Fires can cause damage to the natural sources like vegetation, water and air, or completely eradicate them. They can also cause loss of life and property by damaging the residential areas and cultivated areas around the forests. Therefore, it is important to make the right decisions and make effective planning on using these resources in order to reduce the risk of forest fires and minimize destruction. In this study, Forest Fire Analysis Forecasting (OYAT) model has created to be used in the fight against forest fires. OYAT has built based on previous fires, vegetation, climate changes and human factors datasets obtained from official sources. OYAT has created by processing these datasets with deep learning techniques. By using OYAT, forest fires can be predicted and a regional fire risk map can be forecasted with analyzed datasets. By its compatibility with geographic information systems (GIS), OYAT has a structure that is easy to use, can be updated with dynamic data, and can be visualized and stored. Forest fire data in Turkey between 2013 and 2019 has used for evaluation OYAT. The maps created with OYAT, have got 98% successful ratio. With the growth of the datasets in the following years, it has predicted that OYAT model will be more efficient and successful in planning for fire prevention.

Keywords

• forest fire
• risk analysis
• artificial intelligence
• deep learning

Derin öğrenmeye dayalı orman yangını tahmin modeli geliştirilmesi ve Türkiye yangın risk haritasının oluşturulması

Abstract

Orman yangınları; doğal hayatı, biyolojik ekosistemi tehdit eden ve ormancılık faaliyetlerini sekteye uğratarak ekonomik kayıplara neden olan ana sorunlardan birisidir. Yangınlar; bitki örtüsü, su ve hava gibi doğal kaynakların zarar görmesine veya tamamen yok olmasına neden olabilir. Ayrıca ormanların çevresinde bulunan yerleşim veya tarım alanlarına da hasar vererek can ve mal kayıplarına sebep olabilir. Bu yüzden orman yangınlarıyla mücadele edilmesi ve tahribatın asgari düzeye indirilmesi için kaynakların kullanılmasında doğru kararların verilmesi ve etkili planlamaların yapılması önem arz etmektedir. Bu çalışmada orman yangınlarıyla mücadelede kullanılmak için Orman Yangını Analiz Tahmin (OYAT) modeli oluşturulmuştur. OYAT; resmi kaynaklardan elde edilen bitki örtüsü, iklim değişiklikleri, beşeri etmenler ve daha önceki yangın verilerine dayandırılarak geliştirilmiştir. Bu veriler derin öğrenme tekniği ile işlenerek OYAT modeli oluşturulmuştur. OYAT kullanılarak, analiz edilen veriler ile orman yangını tahmini yapılır ve bölgesel yangın risk haritası elde edilir. OYAT coğrafi bilgi sistemleri (CBS) ile uyumlu çalışabilmesi sayesinde kolay kullanıma sahip, dinamik veriler ile güncellenebilen ve görselleştirilerek saklanabilen bir yapıya sahiptir. Türkiye’deki 2013-2019 yılları arasındaki orman yangını verileri OYAT modelinin değerlendirilmesinde kullanılmıştır. OYAT ile oluşturulan risk haritalarının %98 doğruluk oranına sahip olduğu gözlenmiştir. İzleyen yıllarda veri havuzunun büyümesiyle birlikte, OYAT modelinin yangın önleme için yapılacak planlamalarda daha verimli ve başarılı olacağı öngörülmektedir.

Keywords

• orman yangını
• risk analizi
• yapay zekâ
• derin öğrenme

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