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Dergiler / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 9 Sayı: 1

EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK

Engin Pekel(HITIT UNIVERSITY)0000-0002-5295-8013
Sayfa
186–194
Yayın Tarihi
2020-01-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The numerical results show that hybrid PSO-ANN is applied to estimate SM successfully.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Soil plays a vital role in the climate system.
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Temel Bulgular & Veri:The coefficient of determination and mean absolute error are chosen to measure the performance of the performed hybrid PSO-ANN.
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Sonuç & Katkı:The numerical results show that hybrid PSO-ANN is applied to estimate SM successfully.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:estimation · artificial neural network · particle swarm optimization

Özet

Soil plays a vital role in the climate system. This paper performs a hybrid methodology that consists of particle swarm optimization (PSO) and artificial neural network (ANN) to estimate soil moisture (SM) by considering different parameters that include air temperature, time, relative humidity and soil temperature. Besides, this paper investigates the effects of the parameters of PSO-ANN by utilizing from the response surface. PSO algorithm is involved in the process of changing the weights of ANN. The coefficient of determination and mean absolute error are chosen to measure the performance of the performed hybrid PSO-ANN. The numerical results show that hybrid PSO-ANN is applied to estimate SM successfully.

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Bu Makaleye Atıf Yap

Pekel, E. (2020). EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 9 Sayı: 1, 186–194. https://doi.org/10.28948/ngumuh.529418

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Kaynakça (6)

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  2. Qu Y., Qian X., Song H., Xing Y., Li Z., Tan, J., “Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing”, Water, 10, 423, 2018
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  5. Yang Q., Zuo H., Li W., “Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region”, Plos One, 11(3), 2016
  6. Eberhart R. and Kennedy J., “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium Micro Machine and Human Science, Nagoya, Japan, 39-43, 1995. [7] https://www.utm.utoronto.ca/geography/resources/environmental-datasets, 04.02.2019