A novel feature extraction-selection technique for long lead time agricultural drought forecasting DOI
Mehdi Mohammadi Ghaleni, Mansour Moradi, Mahnoosh Moghaddasi

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132332 - 132332

Опубликована: Ноя. 1, 2024

Язык: Английский

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

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102382 - 102382

Опубликована: Апрель 17, 2025

Язык: Английский

Процитировано

1
Characterizing drought prediction with deep learning: A literature review DOI Creative Commons
Aldo Márquez-Grajales,

Ramiro Villegas-Vega,

Fernando Salas-Martínez

и другие.

MethodsX, Год журнала: 2024, Номер 13, С. 102800 - 102800

Опубликована: Июнь 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.

Язык: Английский

Процитировано

5
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

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 257, С. 124962 - 124962

Опубликована: Авг. 1, 2024

Язык: Английский

Процитировано

4
Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6 DOI Creative Commons
Yudong Wang, Guibin Pang, Tianyu Wang

и другие.

Agriculture, Год журнала: 2024, Номер 14(9), С. 1556 - 1556

Опубликована: Сен. 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.

Язык: Английский

Процитировано

4
Drought prediction in Jilin Province based on deep learning and spatio-temporal sequence modeling DOI

Zhaojun Hou,

Beibei Wang, Yichen Zhang

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 642, С. 131891 - 131891

Опубликована: Авг. 27, 2024

Язык: Английский

Процитировано

3
Innovative Multi-Temporal Evapotranspiration Forecasting Using Empirical Fourier Decomposition and Bidirectional Long Short-Term Memory DOI Creative Commons
Masoud Karbasi, Mumtaz Ali,

Gurjit S. Randhawa

и другие.

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100619 - 100619

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

3
An interpretable machine learning approach reveals the interaction between air pollutants and climate factors on tuberculosis DOI
Shuo Wang, Ziheng Li,

T. Zhang

и другие.

Urban Climate, Год журнала: 2025, Номер 61, С. 102420 - 102420

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

0
A novel feature extraction-selection technique for long lead time agricultural drought forecasting DOI
Mehdi Mohammadi Ghaleni, Mansour Moradi, Mahnoosh Moghaddasi

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132332 - 132332

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

2