RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING

Halit Çetiner; Burhan Kara

doi:10.54365/adyumbd.1075265

EN TR

RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING

Abstract

In the study carried out in line with the stated purposes, monthly rain, humidity and temperature data, wheat production amount, and wheat productivity data of Konya province between 1980-2020 were used. Using these data, wheat productivity estimation was performed with (Gated Recurrent Units) GRU and Long Short Term Memory (LSTM) methods, which are Recurrent Neural Network (RNN) based algorithms. When wheat productivity estimation performance was examined with the implemented GRU-based model, 0.9550, 0.0059, 0.0280, 0.0623, 7.45 values were obtained for the R2 score, MSE, RMSE, MAE and MAPE values, respectively. In the performance results obtained with the LSTM method, which is another RNN-based method, 0.9667, 0.0054, 0.0280, 0.0614, 7.33 values were obtained for the R2 score, MSE, RMSE, MAE and MAPE values, respectively. Although the LSTM method gave better results than the GRU method, the training modelling time of the LSTM method took longer than that of the GRU method.

Keywords

Wheat yield , wheat production , GRU , LSTM , regression analysis

BUĞDAY VERİM TAHMİNİ İÇİN YENİLEMELİ SİNİR AĞI TABANLI MODEL GELİŞTİRME

Öz

Bu çalışmada 1980-2020 yılları arasında Konya ilinin aylık yağış, nem ve sıcaklık verileri, buğday üretim miktarı ve buğday verimlilik verileri kullanılmıştır. Bu veriler kullanılarak Recurrent Neural Network (RNN) tabanlı algoritmalar olan (Gated Recurrent Units) GRU ve Long Short Term Memory (LSTM) yöntemleri ile buğday verimlilik tahmini yapılmıştır. Gerçekleştirilen GRU tabanlı model ile buğday verimliliği tahmin performansları incelendiğinde R2 puan, MSE, RMSE, MAE ve MAPE değerleri için sırasıyla 0.9550, 0.0059, 0.0280, 0.0623, 7.45 değerleri elde edilmiştir. RNN tabanlı bir diğer yöntem olan LSTM yöntemiyle elde edilen performans sonuçlarında ise R2 puan, MSE, RMSE, MAE ve MAPE değerleri için sırasıyla 0.9667, 0.0054, 0.0280, 0.0614, 7.33 değerleri elde edilmiştir. LSTM yöntemi, GRU yönteminden daha iyi sonuçlar vermesine rağmen LSTM yönteminin eğitim modelleme süresi GRU yönteminden daha fazla sürmüştür.

Anahtar Kelimeler

Buğday verimi , buğday üretimi , GRU , LSTM , regresyon analizi

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

Halit Çetiner ^*
0000-0001-7794-2555
Türkiye

Burhan Kara
0000-0002-4207-0539
Türkiye

Yayımlanma Tarihi

14 Nisan 2022

Gönderilme Tarihi

17 Şubat 2022

Kabul Tarihi

11 Nisan 2022

Yayımlandığı Sayı

Yıl 1970 Cilt: 9 Sayı: 16

DOI

https://doi.org/10.54365/adyumbd.1075265

IZ

https://izlik.org/JA98HW53KK

APA

Çetiner, H., & Kara, B. (2022). RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 9(16), 204-218. https://doi.org/10.54365/adyumbd.1075265

AMA

1.Çetiner H, Kara B. RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9(16):204-218. doi:10.54365/adyumbd.1075265

Chicago

Çetiner, Halit, ve Burhan Kara. 2022. “RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9 (16): 204-18. https://doi.org/10.54365/adyumbd.1075265.

EndNote

Çetiner H, Kara B (01 Nisan 2022) RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9 16 204–218.

IEEE

[1]H. Çetiner ve B. Kara, “RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 9, sy 16, ss. 204–218, Nis. 2022, doi: 10.54365/adyumbd.1075265.

ISNAD

Çetiner, Halit - Kara, Burhan. “RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 9/16 (01 Nisan 2022): 204-218. https://doi.org/10.54365/adyumbd.1075265.

JAMA

1.Çetiner H, Kara B. RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2022;9:204–218.

MLA

Çetiner, Halit, ve Burhan Kara. “RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, c. 9, sy 16, Nisan 2022, ss. 204-18, doi:10.54365/adyumbd.1075265.

Vancouver

1.Halit Çetiner, Burhan Kara. RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 01 Nisan 2022;9(16):204-18. doi:10.54365/adyumbd.1075265

BUĞDAY VERİM TAHMİNİ İÇİN YENİLEMELİ SİNİR AĞI TABANLI MODEL GELİŞTİRME

RECURRENT NEURAL NETWORK BASED MODEL DEVELOPMENT FOR WHEAT YIELD FORECASTING

Abstract

Keywords

BUĞDAY VERİM TAHMİNİ İÇİN YENİLEMELİ SİNİR AĞI TABANLI MODEL GELİŞTİRME

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

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