Cited by A novel feature extraction-selection technique for long lead time agricultural drought forecasting

Characterizing drought prediction with deep learning: A literature review DOI

Aldo Márquez-Grajales,

Ramiro Villegas-Vega,

Fernando Salas-Martínez

et al.

MethodsX, Journal Year: 2024, Volume and Issue: 13, P. 102800 - 102800

Published: June 13, 2024

Drought prediction is a complex phenomenon that impacts human activities and the environment. For this reason, predicting its behavior crucial to mitigating such effects. Deep learning techniques are emerging as powerful tool for task. The main goal of work review state-of-the-art characterizing deep used in drought results suggest most widely climate indexes were Standardized Precipitation Index (SPI) Evapotranspiration (SPEI). Regarding multispectral index, Normalized Difference Vegetation (NDVI) indicator utilized. On other hand, countries with higher production scientific knowledge area located Asia Oceania; meanwhile, America Africa regions few publications. Concerning methods, Long-Short Term Memory network (LSTM) algorithm implemented task, either canonically or together (hybrid methods). In conclusion, reveals need more about using indices Africa; therefore, it an opportunity characterize developing countries.

Language: Английский

Citations

Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6 DOI

Yudong Wang, Guibin Pang, Tianyu Wang

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(9), P. 1556 - 1556

Published: Sept. 9, 2024

One of the primary factors in hydrological cycle is reference evapotranspiration (ET0). The prediction ET0 crucial to manage irrigation water agriculture under climate change; however, little research has been conducted on trends changes Shandong Province. In this study, estimate entire Province, 245 sites were chosen, and monthly values during 1901–2020 computed using Hargreaves–Samani formula. A deep learning model, termed SAO-CNN-BiGRU-Attention, was utilized forecast 2021–2100, predictions compared two CMIP6 scenarios, SSP2-4.5 SSP5-8.5. hierarchical clustering results revealed that Province encompassed three homogeneous regions. Clusters H1 H2, which situated inland regions major agricultural areas, highest. SAO-CNN-BiGRU-Attention SSP5-8.5 forecasting generally displayed a monotonically growing trend period regions; model declining tendency at few points. According results, 2091–2100, H1, H3 will reach their peaks; show peak 2031–2040. At end period, for H3, rate increased by 1.31, 1.56%, 1.80%, respectively, whereas SSP2-4.5’s 0.31%, 0.95%, 1.57%, SSP5-8.5’s 10.88%, 10.76%, 10.69%, respectively. similar those (R2 > 0.96). can be used future ET0.

Language: Английский

Citations

Enhancing multi-temporal drought forecasting accuracy for Iran: Integrating an innovative hidden pattern identifier, recursive feature elimination, and explainable ensemble learning DOI

Mahnoosh Moghaddasi,

Mansour Moradi,

Mehdi Mohammadi Ghaleni

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102382 - 102382

Published: April 17, 2025

Language: Английский

Citations

An interpretable machine learning approach reveals the interaction between air pollutants and climate factors on tuberculosis DOI

Shuo Wang, Ziheng Li,

T. Zhang

et al.

Urban Climate, Journal Year: 2025, Volume and Issue: 61, P. 102420 - 102420

Published: April 22, 2025

Language: Английский

Citations

Wavelet-Based precipitation preprocessing for improved drought Forecasting: A Machine learning approach using tunable Q-factor wavelet transform and maximum overlap discrete wavelet transform DOI

Shabbir Ahmed Osmani, Changhyun Jun, Jongjin Baik

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 257, P. 124962 - 124962

Published: Aug. 1, 2024

Language: Английский

Citations

Drought prediction in Jilin Province based on deep learning and spatio-temporal sequence modeling DOI

Zhaojun Hou,

Beibei Wang, Yichen Zhang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 642, P. 131891 - 131891

Published: Aug. 27, 2024

Language: Английский

Citations

Innovative Multi-Temporal Evapotranspiration Forecasting Using Empirical Fourier Decomposition and Bidirectional Long Short-Term Memory DOI

Masoud Karbasi, Mumtaz Ali,

Gurjit S. Randhawa

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100619 - 100619

Published: Oct. 1, 2024

Language: Английский

Citations

A novel feature extraction-selection technique for long lead time agricultural drought forecasting DOI

Mehdi Mohammadi Ghaleni, Mansour Moradi, Mahnoosh Moghaddasi

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132332 - 132332

Published: Nov. 1, 2024

Language: Английский

Citations