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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An improved SPEI drought forecasting approach using the long short-term memory neural network
Abhirup Dikshit, Biswajeet Pradhan, Alfredo Huete
Journal of Environmental Management (2021) Vol. 283, pp. 111979-111979
Open Access | Times Cited: 129

Showing 1-25 of 129 citing articles:

Interpretable and explainable AI (XAI) model for spatial drought prediction
Abhirup Dikshit, Biswajeet Pradhan
The Science of The Total Environment (2021) Vol. 801, pp. 149797-149797
Closed Access | Times Cited: 152
Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms
Ali Mokhtar, Mohammadnabi Jalali, Hongming He, et al.
IEEE Access (2021) Vol. 9, pp. 65503-65523
Open Access | Times Cited: 143
Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting
Dehe Xu, Qi Zhang, Yan Ding, et al.
Environmental Science and Pollution Research (2021) Vol. 29, Iss. 3, pp. 4128-4144
Open Access | Times Cited: 106
Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm
Babak Mohammadi
Hydrology (2023) Vol. 10, Iss. 3, pp. 58-58
Open Access | Times Cited: 50
Artificial neural networks in drought prediction in the 21st century–A scientometric analysis
Abhirup Dikshit, Biswajeet Pradhan, M. Santosh
Applied Soft Computing (2021) Vol. 114, pp. 108080-108080
Closed Access | Times Cited: 96
Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability
Qiang Wang, Yang Dai, Zhanguo Ma, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 1, pp. 588-604
Closed Access | Times Cited: 28
Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms
Fabio Di Nunno, Francesco Granata
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2349-2375
Closed Access | Times Cited: 25
Leveraging multisource data for accurate agricultural drought monitoring: A hybrid deep learning model
Xin Xiao, Wenting Ming, Xuan Luo, et al.
Agricultural Water Management (2024) Vol. 293, pp. 108692-108692
Open Access | Times Cited: 14
Dynamic-LSTM hybrid models to improve seasonal drought predictions over China
Zhiyong Wu, Hao Yin, Hai He, et al.
Journal of Hydrology (2022) Vol. 615, pp. 128706-128706
Closed Access | Times Cited: 37
Modelling agricultural drought: a review of latest advances in big data technologies
Ismaguil Hanadé Houmma, Loubna El Mansouri, Sébastien Gadal, et al.
Geomatics Natural Hazards and Risk (2022) Vol. 13, Iss. 1, pp. 2737-2776
Open Access | Times Cited: 29
Data-driven models for atmospheric air temperature forecasting at a continental climate region
Mohamed Khalid AlOmar, Faidhalrahman Khaleel, Mustafa Mohammed Aljumaily, et al.
PLoS ONE (2022) Vol. 17, Iss. 11, pp. e0277079-e0277079
Open Access | Times Cited: 28
LSTM-CM: a hybrid approach for natural drought prediction based on deep learning and climate models
Tuong Quang Vo, Seon‐Ho Kim, Duc Hai Nguyen, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2035-2051
Closed Access | Times Cited: 20
A new integrated index for drought stress monitoring based on decomposed vegetation response factors
Guoying Yin, Hongyan Zhang
Journal of Hydrology (2023) Vol. 618, pp. 129252-129252
Closed Access | Times Cited: 19
A new deep learning method for meteorological drought estimation based-on standard precipitation evapotranspiration index
Sercan Yalçın, Musa Eşit, Önder Çoban
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106550-106550
Closed Access | Times Cited: 19
Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States
Mohammad Valipour, Helaleh Khoshkam, Sayed M. Bateni, et al.
Agricultural Water Management (2023) Vol. 283, pp. 108311-108311
Open Access | Times Cited: 17
Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models
Sunil Saha, Barnali Kundu, Gopal Chandra Paul, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 7, pp. 2513-2540
Open Access | Times Cited: 16
Standardized precipitation evapotranspiration index (SPEI) estimated using variant long short-term memory network at four climatic zones of China
Juan Dong, Liwen Xing, Ningbo Cui, et al.
Computers and Electronics in Agriculture (2023) Vol. 213, pp. 108253-108253
Closed Access | Times Cited: 16
Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records
Xiaoyi Wang, Gerald Corzo, Haishen Lü, et al.
Agricultural Water Management (2024) Vol. 295, pp. 108772-108772
Open Access | Times Cited: 7
Could the Russia-Ukraine war stir up the persistent memory of interconnectivity among Islamic equity markets, energy commodities, and environmental factors?
حسن حیدری, Sami Ben Jabeur
Research in International Business and Finance (2024) Vol. 69, pp. 102260-102260
Closed Access | Times Cited: 6
A Novel Fusion-Based Methodology for Drought Forecasting
Huihui Zhang, Hugo A. Loáiciga, Tobias Sauter
Remote Sensing (2024) Vol. 16, Iss. 5, pp. 828-828
Open Access | Times Cited: 6
Explainable AI in drought forecasting
Abhirup Dikshit, Biswajeet Pradhan
Machine Learning with Applications (2021) Vol. 6, pp. 100192-100192
Open Access | Times Cited: 37
SPI-Based Hybrid Hidden Markov–GA, ARIMA–GA, and ARIMA–GA–ANN Models for Meteorological Drought Forecasting
Mohammed Alquraish, Khaled Ali Abuhasel, Abdulrahman Saad Alqahtani, et al.
Sustainability (2021) Vol. 13, Iss. 22, pp. 12576-12576
Open Access | Times Cited: 34
Multi-scale approach for different type of drought in temperate climatic conditions
Ionuț Minea, Marina Iosub, Daniel Boicu
Natural Hazards (2021) Vol. 110, Iss. 2, pp. 1153-1177
Open Access | Times Cited: 33
Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation
Anbang Peng, Xiaoli Zhang, Wei Xu, et al.
Water Resources Management (2022) Vol. 36, Iss. 7, pp. 2381-2394
Closed Access | Times Cited: 27
Projection of droughts in Amu river basin for shared socioeconomic pathways CMIP6
Obaidullah Salehie, Mohammed Magdy Hamed, Tarmizi Ismail, et al.
Theoretical and Applied Climatology (2022) Vol. 149, Iss. 3-4, pp. 1009-1027
Closed Access | Times Cited: 24
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