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Journals / GSI Journals Serie C: Advancements in Information Sciences and Technologies / 2026 / Cilt: 8 Sayı: 2

Operationalizing Nature Based Intelligence for Ecosystem Early Warning

Ekosistem Erken Uyarı Sistemi için Doğa Temelli Zekanın Uygulanması

Alper Çabuk(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ)0000-0002-0684-2247
Alp Deniz Çabuk(Technische Universität München)0009-0004-6117-8504
Volkan Turkbay0009-0008-9445-4551
Halil Duymuş(ÇUKUROVA ÜNİVERSİTESİ)0000-0002-8113-9674
Pages
53–67
Publication Date
2026-10-29
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Temporal and site-transfer performance will be evaluated separately.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Environmental early-warning systems often treat data quality, anomaly detection, adaptation, and provenance as separate tasks.
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Key Findings & Data:The engineering precursor, XAI Kernel v11, passes 48 unit and regression tests; these checks concern software behaviour, and ecological performance remains untested.
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Conclusion & Impact:Temporal and site-transfer performance will be evaluated separately.
🔬Design:Araştırma Makalesi
📍Scope:2018 Verileri
⚙️Variables:Doğaya Temelli Zeka · ekolojik erken uyarı · kuraklık

Abstract

Environmental early-warning systems often treat data quality, anomaly detection, adaptation, and provenance as separate tasks. That separation is problematic in non-stationary ecosystems, where unusual observations may represent either unreliable data or legitimate ecological novelty, and where unrestricted adaptation can normalize persistent degradation. We introduce Nature-Based Intelligence (NBI) as a computational decision-intelligence paradigm grounded in five operational principles: multiscale memory, context-conditioned normality, relational coherence, conservative adaptation, and auditable feedback. We formalize these principles in NBI-EW, an early-warning architecture combining fast and slow state representations, Mahalanobis stress energy, debiased Sinkhorn distributional displacement, optional spatial coherence, gated reference updating, and tamper-evident provenance. The engineering precursor, XAI Kernel v11, passes 48 unit and regression tests; these checks concern software behaviour, and ecological performance remains untested. We specify a leakage-controlled validation protocol using the ICOS Drought-2018 eddy-covariance product to test early detection of drought-associated functional stress at matched false-alarm rates. Temporal and site-transfer performance will be evaluated separately.

Özet

Çevresel erken uyarı sistemleri çoğu zaman veri kalitesi, anomali saptama, uyarlama ve veri kökenini birbirinden bağımsız görevler olarak ele almaktadır. Bu ayrım durağan olmayan ekosistemlerde sorun yaratır; çünkü alışılmadık gözlemler güvenilir olmayan verilerden ya da gerçek ekolojik yeniliklerden kaynaklanabilir ve sınırsız uyarlama kalıcı bozulmayı olağan duruma dönüştürebilir. Bu çalışmada Doğa Temelli Zekâ (Nature-Based Intelligence, NBI), beş işlemsel ilkeye dayanan hesaplamalı bir karar zekâsı yaklaşımı olarak sunulmaktadır: çok ölçekli bellek, bağlama koşullu normallik, ilişkisel tutarlılık, ihtiyatlı uyarlama ve denetlenebilir geri bildirim. Bu ilkeler; hızlı ve yavaş durum temsillerini, Mahalanobis stres enerjisini, yanlılığı giderilmiş Sinkhorn dağılımsal yer değiştirmesini, isteğe bağlı mekânsal tutarlılığı, kapı denetimli referans güncellemesini ve değişikliğin izini belirgin kılan veri kökeni kaydını birleştiren NBI-EW erken uyarı mimarisinde biçimselleştirilmektedir. Mühendislik öncülü XAI Kernel v11, toplam 48 birim ve regresyon testini geçmiştir; ancak bu kontroller yalnızca yazılım davranışına ilişkindir ve ekolojik performans henüz sınanmamıştır. Kuraklıkla ilişkili işlevsel stresin, eşleştirilmiş yanlış alarm oranlarında ne kadar erken saptanabildiğini değerlendirmek amacıyla ICOS Drought-2018 girdap kovaryans ürününe dayalı, veri sızıntısını kontrol eden bir doğrulama protokolü tanımlanmaktadır. Zamansal aktarım ve istasyonlar arası aktarım ayrı değerlendirilecektir.

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Cite This Article

Çabuk, A., Çabuk, A. D., Turkbay, V., Duymuş, H. (2026). Operationalizing Nature Based Intelligence for Ecosystem Early Warning. GSI Journals Serie C: Advancements in Information Sciences and Technologies, Cilt: 8 Sayı: 2, 53–67. https://ajindex.com/articles/1667989

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