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Dergiler / Journal of Agricultural Sciences / 2026 / Volume: 31 Issue: 2

Evaporation and precipitation prediction for future time frames via combined machine learning-climate change models: Quri Gol Wetland Case

Mohammad Reza Abdollahpour Azad(Islamik Azad University - Arak Branch)0009-0002-6924-361X
Mohammad Reza Jalali(Islamik Azad University - Arak Branch)0000-0003-0826-8303
Mohammad Taghi Sattari(University of Tabriz)0000-0002-5139-2118
Reza Mastouri(Islamik Azad University - Arak Branch)0000-0003-0870-040X
Sayfa
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Yayın Tarihi
2025-03-25
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The findings emphasize the need for adaptive water management strategies to mitigate the effects of increased evaporation and maintain ecological stability in the region.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Evaporation is a critical component in the management of water resources.
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Temel Bulgular & Veri:The applicability and performance of these models vary depending on the specific climatic conditions of each region.
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Sonuç & Katkı:The findings emphasize the need for adaptive water management strategies to mitigate the effects of increased evaporation and maintain ecological stability in the region.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2020 Verileri
⚙️Değişkenler:Climate change · Meteorological variables · Evaporation

Abstract

Evaporation is a critical component in the management of water resources. Due to the complex interactions between various meteorological variables involved in evaporation calculations, numerous nonlinear models have been developed. The applicability and performance of these models vary depending on the specific climatic conditions of each region. This study evaluates the impacts of climate change on evaporation and precipitation patterns in the Quri Gol Wetland, located in East Azerbaijan, Iran, using machine learning models and climate change projections. Evaporation values for the present period (1991-2020) were estimated using six machine learning models: Random Forest (RF), Gradient Boosted Tree (GBT), Generalized Linear Model (GLM), Support Vector Machine (SVM), Gaussian Process Regression (GPR), and deep learning (DL). Future projections (2021-2050, 2051- 2080, 2081-2100) were based on the LARS-WG and SDSM models under three climate scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The performance of the machine learning models was assessed using statistical metrics including R2, Scatter Index (SI), Mean Absolute Error (MAE), Willmott’s Index (WI), and Kling-Gupta Efficiency (KGE). The RF and DL models provided the most accurate predictions, with RF achieving an R2 of 0.821 and an MAE of 0.902, while DL reached an R2 of 0.822 and an MAE of 0.915 in the validation phase. Results from climate change projections indicated a significant increase in evaporation over the next century, with cumulative evaporation rising by up to 50.01% under the RCP 8.5 scenario by 2081-2100. In contrast, the projected increase in precipitation was much smaller, reaching a maximum of 16% in the same period. This imbalance between evaporation and precipitation highlights the potential for increasing water stress in the Quri Gol Wetland. The findings emphasize the need for adaptive water management strategies to mitigate the effects of increased evaporation and maintain ecological stability in the region.

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Bu Makaleye Atıf Yap

Azad, M. R. A., Jalali, M. R., Sattari, M. T., Mastouri, R. (2025). Evaporation and precipitation prediction for future time frames via combined machine learning-climate change models: Quri Gol Wetland Case. Journal of Agricultural Sciences, Volume: 31 Issue: 2. https://doi.org/10.15832/ankutbd.1509731

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