The future of agricultural lands under the combined influence of shared socioeconomic pathways and urban expansion by 2050 DOI

Ali Sadian,

Hossein Shafizadeh‐Moghadam

Agricultural Systems, Journal Year: 2024, Volume and Issue: 224, P. 104234 - 104234

Published: Dec. 20, 2024

Language: Английский

Dissolved Organic Carbon Estimation in Lakes: Improving Machine Learning with Data Augmentation on Fusion of Multi-Sensor Remote Sensing Observations DOI

Seyed Babak Haji Seyed Asadollah,

Ahmadreza Safaeinia,

Sina Jarahizadeh

et al.

Water Research, Journal Year: 2025, Volume and Issue: 277, P. 123350 - 123350

Published: Feb. 22, 2025

Language: Английский

Citations

2
Koreksi Bias Data Hujan Proyeksi Coupled Model Intercomparison Project Phase 6 (Cmip6) Di Kota Bima DOI Creative Commons

I Putu Hartawan,

Muhamad Zaky Ibnu Malik,

Laifhan Setyo Qhairaan

et al.

Cerdika Jurnal Ilmiah Indonesia, Journal Year: 2025, Volume and Issue: 5(1), P. 57 - 66

Published: Jan. 15, 2025

Perubahan iklim global telah memengaruhi pola curah hujan, khususnya di wilayah tropis, termasuk Kota Bima. Model proyeksi seperti Coupled Intercomparison Project Phase 6 (CMIP6) menyediakan data penting untuk memprediksi perubahan iklim, namun sering kali mengandung bias yang signifikan. Penelitian ini bertujuan melakukan koreksi pada hujan CMIP6 menggunakan lima model dalam skenario SSP5-8.5, yaitu CMCC-CM2-SR5, CESM2-WACCM, ACCESS-CM2, CESM2, dan AWI-CM-1-1-MR, dengan mengintegrasikan historis dari Global Precipitation Climatology Centre (GPCC) lokal BMKG. Hasil penelitian menunjukkan bahwa GPCC memiliki korelasi sangat kuat BMKG, nilai koefisien sebesar 0,97 RMSE 34,41 mm. empat (CESM2-WACCM, AWI-CM-1-1-MR) tren serupa berdasarkan analisis Weibull plotting. Sementara itu, CMCC-CM2-SR5 penyimpangan Implikasi adalah meningkatkan akurasi mendukung perencanaan mitigasi risiko bencana pengelolaan sumber daya air juga membuka peluang pengembangan metode lebih efisien teknologi pembelajaran mesin rinci.

Citations

0
Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data DOI
Ali Asghar Zolfaghari,

Maryam Raeesi,

Giuseppe Longo-Minnolo

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102343 - 102343

Published: March 30, 2025

Language: Английский

Citations

0
Enhancing Precipitation Intensity Estimation Using ERA5-Land Reanalysis with Statistical and Machine Learning Approaches DOI Creative Commons
Alireza Abdolmanafi, Bahram Saghafian, Saleh Aminyavari

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104928 - 104928

Published: April 1, 2025

Language: Английский

Citations

0
The future of agricultural lands under the combined influence of shared socioeconomic pathways and urban expansion by 2050 DOI

Ali Sadian,

Hossein Shafizadeh‐Moghadam

Agricultural Systems, Journal Year: 2024, Volume and Issue: 224, P. 104234 - 104234

Published: Dec. 20, 2024

Language: Английский

Citations

0