LSTM AND ANFIS MACHINE LEARNING ALGORITHMS IN ESTIMATING THE SEA WATER TEMPERATURE IN TÜRKİYE AT VARIOUS SEA LOCATIONS

Akın İlhan; Sergen Tümse; Mehmet Bilgili; Alper Yıldırım; Beşir Şahin

doi:10.17780/ksujes.1562465

EN TR

LSTM AND ANFIS MACHINE LEARNING ALGORITHMS IN ESTIMATING THE SEA WATER TEMPERATURE IN TÜRKİYE AT VARIOUS SEA LOCATIONS

Abstract

The World's temperature is experiencing a rapid increase, leading to negative consequences for aquatic ecosystems such as oceans, seas, lakes, and rivers. There are also other negative influences consisting of changing precipitation patterns, disruptions in marine current circulation, and formation of negative impacts on marine life. Ultimately, there is a compelling need for careful monitoring of sea temperatures to understand and address these interconnected environmental changes. The daily temperature of seawater (SWT) is a crucial abiotic variable that changes both the chemical composition of water and aquatic life in seas and oceans. The present study explored the capabilities of artificial intelligence techniques in one-day-ahead SWT predictions. These techniques are fuzzy c-means adaptive neuro-fuzzy inference system (ANFIS-FCM), subtractive clustering ANFIS (ANFIS-SC), grid segmentation ANFIS (ANFIS-GP), and long short-term memory (LSTM) and artificial neural network (ANN). Accordingly, daily SWT data that was collected from Alanya, Bodrum, and Akcakoca measurement stations located in Türkiye's Mediterranean, Aegean, and Black Sea locations were used in SWT predictions. Estimated results obtained by these five estimation methods were compared to the real observed values by interpreting four statistical metrics. Consequently, the most accurate estimates were obtained utilizing the fuzzy c-means (FCM) of ANFIS. Besides, it was reported that the LSTM approach closely followed the accuracy of this prediction of FCM. Both proposed models have generated superior statistical accuracy results corresponding to 0.34% MAPE, 0.0765 oC MAE, 0.1585 oC RMSE, and 0.9990 R. Those results have indicated the closest match of the predictions on the real measured data that have been acquired by ANFIS-FCM and LSTM models.

Keywords

TÜRKİYE’DE FARKLI DENİZ MEVKİLERİNDEKİ DENİZ SUYU SICAKLIĞININ LSTM VE ANFIS MAKİNE ÖĞRENMESİ ALGORİTMALARI KULLANILARAK TAHMİN EDİLMESİ

Öz

Dünyanın sıcaklığında hızlı bir artış yaşanmaktadır ve bu durum, yağış düzenlerinde değişimlere, deniz akıntısı dolaşımında bozulmalara ve deniz yaşamında olumsuz etkilere neden olur. Ayrıca, bu durumun, okyanuslar, denizler, göller ve nehirler gibi su ekosistemleri üzerinde diğer olumsuz etkileri de bulunmaktadır. Sonuçta, birbiriyle bağlantılı olan bu çevresel değişiklikleri anlamak ve ele almak için deniz sıcaklıklarının dikkatli bir şekilde izlenmesine ve yorumlanmasına zorunlu bir ihtiyaç vardır. Diğer taraftan, deniz suyunun günlük sıcaklıkları (SWT), denizlerdeki ve okyanuslardaki hem suyun hem de su yaşamının kimyasal bileşimini değiştiren çok önemli bir abiyotik değişkendir. Bu çerçevede, bu çalışma, bir gün sonrası SWT tahminlerinde yapay zekâ tekniklerinin kabiliyetlerini araştırmıştır. Bu teknikler, bulanık c-ortalamalar uyarlamalı nöro-bulanık çıkarım sistemi (ANFIS-FCM), çıkarımlı kümeleme ANFIS (ANFIS-SC), ızgara bölümleme ANFIS (ANFIS-GP) ve uzun kısa süreli bellek (LSTM) sinir ağı ve yapay sinir ağıdır (ANN). Bu doğrultuda, Türkiye'nin Akdeniz’de yer alan Alanya, Ege’de bulunan Bodrum ve Karadeniz’de kurulmuş olan Akçakoca ölçüm istasyonlarından elde edilen günlük SWT verileri kullanılmıştır. Beş farklı tahmin yöntemi kullanılarak üretilen tahmin sonuçları, gerçek gözlemlenen değerlere göre dört farklı istatistiksel ölçüm yaklaşımı kullanılarak karşılaştırıldı ve yorumlandı. Neticede, en doğru tahminlerin, ANFIS’in bulanık c-ortalamalar (FCM) yöntemi kullanıldığında elde edildiği sonucuna varılmıştır. Bu modeli en yakından takip eden ikinci en iyi modelin ise; LSTM yaklaşımı olduğu sonucuna varılmış ve bulunan sonuçlar rapor edilmiştir. Önerilen bu iki model de %0,34’lük MAPE, 0,0765 oC’lik MAE, 0,1585 oC’lik RMSE ve 0,9990’lık R'ye karşılık gelen üstün istatistiksel doğruluk sonuçlarını üretmiştir. Bu sonuçlar, gerçek ölçülen verilere en yakın eşleşmelerin ANFIS-FCM ve LSTM modelleri ile elde edildiğini göstermiştir.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Makine Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Akın İlhan ^*
0000-0003-3590-5291
Türkiye

Sergen Tümse
0000-0003-4764-747X
Türkiye

Mehmet Bilgili
0000-0002-5339-6120
Türkiye

Alper Yıldırım
0000-0003-2626-1666
Türkiye

Beşir Şahin
0000-0003-0671-0890
Türkiye

Yayımlanma Tarihi

3 Mart 2025

Gönderilme Tarihi

6 Ekim 2024

Kabul Tarihi

22 Şubat 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1562465

IZ

https://izlik.org/JA46AN67SU

Kaynak Göster

RIS / Bibtex

APA

İlhan, A., Tümse, S., Bilgili, M., Yıldırım, A., & Şahin, B. (2025). LSTM AND ANFIS MACHINE LEARNING ALGORITHMS IN ESTIMATING THE SEA WATER TEMPERATURE IN TÜRKİYE AT VARIOUS SEA LOCATIONS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 322-333. https://doi.org/10.17780/ksujes.1562465

Cited By

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Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.17780/ksujes.1747540