Generalized Multiprocess Kbest-Based Expert System for Improved Multitemporal Evapotranspiration Forecasting in California, United States DOI
Jinwook Lee, Sayed M. Bateni, Changhyun Jun

et al.

Published: Jan. 1, 2023

Evapotranspiration is an essential component of the hydrological cycle. Forecasting reference crop evapotranspiration (ETo) using a reliable and generalized framework crucial for agricultural operations, especially irrigation. This study was aimed at evaluating performance multivariate-multitemporal intelligent system including K-Best selection (KBest), multivariate variational mode decomposition (MVMD), cascade forward neural network (CFNN) 1-, 3-, 7-, 10-day-ahead forecasting daily ETo in twelve stations California, one significant regions U.S. The input variables included solar radiation, maximum temperature, minimum average dew point, vapor pressure, relative humidity. analysis covered span 20 years, from 2003 to 2022. In additional CFNN, two other machine learning models, namely, extreme (ELM) bagging regression tree (BRT), were integrated with various preprocessing techniques construct three hybrid i.e., MVMD-KBest-CFNN, MVMD-KBest-ELM, MVMD-KBest-BRT. Using MVMD technique, antecedent information features factorized into intrinsic functions residuals. Subsequently, most influential sub-components filtered KBest reduce computational cost enhance accuracy before inputting models. Several statistical indices, such as correlation coefficient (R) root mean square error (RMSE), used addition diagnostic validation methods assess robustness frameworks standalone According results obtained testing phase, averaged across all stations, MVMD-KBest-CFNN MVMD-KBest-ELM models outperformed MVMD-KBest-BRT model, R values 0.983, 0.980, 0.977, 0.968 forecasts, respectively. corresponding RMSE 0.390, 0.416, 0.450, 0.517 mm/d, demonstrating commendable prediction even longer lead times.

Language: Английский

Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China DOI Creative Commons

Juan Dong,

Liwen Xing, Ningbo Cui

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 292, P. 108665 - 108665

Published: Jan. 9, 2024

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, productivity, and irrigation systems. As the standard ET0 method, Penman-Monteith equation has been widely recommended worldwide. However, its application still restricted to comprehensive meteorological data deficiency, making exploration of alternative simpler models acceptable highly meaningful. Concerning aforementioned requirement, this study developed novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, regression component, estimate based on radiation-based (Rn-based), humidity-based (RH-based), temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), empirical models, result indicated that MA-CNN-BiLSTM achieved superior precision, with values Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), (RMSE), Absolute (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 d−1 strategy 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 0.294–0.699 strategy. Specifically, Rn-based excelled in temperate continental zone (TCZ) mountain plateau (MPZ), while RH-based yielded best precision others. Furthermore, was by 2.74–106.04% R2, 1.11–120.49% NSE, 1.41–40.27% RRMSE, 1.68–45.53% RMSE, 1.21–38.87% MAE, respectively. In summary, main contribution present proposal LSTM-type (MA-CNN-BiLSTM) cope various data-missing scenarios China, can provide effective support decision-making regional agriculture management.

Language: Английский

Citations

18
Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis DOI Creative Commons
Nuruzzaman Faruqui, Mohammad Abu Yousuf, Faris Kateb

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e21520 - e21520

Published: Oct. 27, 2023

The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered Convolutional Neural Network (CNN)-based classifiers. Critical areas study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial improve technology's usability-a factor often neglected in current state-of-the-art research. Yet, this frequently overlooks need for expediting process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired Software-As-A-Service (SAAS) within cloud computing paradigm. As comprehensive service system, HAAS potential reduce mortality rates providing early opportunities everyone. We present HAASNet, cloud-compatible CNN that boasts accuracy rate 96.07%. By integrating HAASNet with physio-symptomatic data Internet Medical Things (IoMT), proposed model generates accurate reliable reports. Leveraging IoMT technology, globally accessible via Internet, transcending geographic boundaries. groundbreaking achieves average precision, recall, F1-scores 96.47%, 95.39%, 94.81%, respectively.

Language: Английский

Citations

33
Advanced evapotranspiration forecasting in Central Italy: Stacked MLP-RF algorithm and correlated Nystrom views with feature selection strategies DOI
Francesco Granata, Fabio Di Nunno, Giovanni de Marinis

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 220, P. 108887 - 108887

Published: March 28, 2024

Language: Английский

Citations

15
Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models DOI

Stephen Luo Sheng Yong,

Jing Lin Ng, Yuk Feng Huang

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(11), P. 4213 - 4241

Published: May 11, 2024

Language: Английский

Citations

10
Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting DOI
Jinwook Lee, Sayed M. Bateni, Changhyun Jun

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 135, P. 108744 - 108744

Published: June 3, 2024

Language: Английский

Citations

9
A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models DOI

Md Mahfuz Alam,

Mst. Yeasmin Akter,

Abu Reza Md. Towfiqul Islam

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119714 - 119714

Published: Dec. 5, 2023

Language: Английский

Citations

17
Untargeted metabolomics‐based identification of bioactive compounds from Mangifera indica L. seed extracts in drug discovery through molecular docking and assessment of their anticancer potential DOI
Mital J. Kaneria, Kalpna Rakholiya,

Kaushal R Bavaliya

et al.

Journal of the Science of Food and Agriculture, Journal Year: 2024, Volume and Issue: 104(10), P. 5907 - 5920

Published: Feb. 28, 2024

Abstract BACKGROUND Mangifera indica L. (mango), a medicinal plant rich in biologically active compounds, has potential to be used disease‐preventing and health‐promoting products. The present investigation reveals uncovers bioactive metabolites with remarkable therapeutic efficiency from mango (family: Anacardiaceae) seeds. RESULTS Biological activity was determined by antimicrobial, antioxidant anticancer assays, metabolite profiling performed on gas chromatography coupled quadrupole time‐of‐flight mass spectrometry (GC‐QTOF‐MS) liquid (LC‐QTOF‐MS) platforms. Validation of carried out silico molecular docking (Molinspiration Cheminformatics Server PASS). Extracted identified were screened; 54 compounds associated various groups selected for the interaction study. CONCLUSIONS Molecular revealed lead molecules binding energy score, efficacy stable modulation protein domain. Investigation, directed vitro analysis, confirms seeds as an excellent source agent. © 2024 Society Chemical Industry.

Language: Английский

Citations

5
Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China DOI Creative Commons

Juan Dong,

Liwen Xing, Ningbo Cui

et al.

Agricultural Water Management, Journal Year: 2023, Volume and Issue: 291, P. 108620 - 108620

Published: Dec. 12, 2023

Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives Penman-Monteith equation, empirical methods have been widely recommended worldwide. However, its application still limited to parameters localization varied with geographical and climatic conditions, therefore developing an excellent optimization algorithm calibrating very necessary. Regarding above requirement, present study developed a novel improved Grey Wolf Algorithm (MDSL-GWA) optimize most ones among three types ETo methods. After performance comparison Least Square Method (LSM), Genetic (GA), (GWA), MDSL-GWA in four regions China, this found that Priestley-Taylor (PT) method was best radiation-based (Rn-based) achieved better temperate continental region (TCR), mountain plateau (MPR), monsoon (TMR) than other types. While temperature-based (T-based) Hargreaves-Samani (HS) performed subtropical (SMR), further attaching same type TMR TCR, while Oudin T-based MPR. Moreover, Romanenko humidity-based (RH-based) TCR MPR, whereas Brockamp-Wenner exhibited higher SMR TMR. Furthermore, despite intelligence algorithms significantly enhancing original methods, outperformed by 4.5–29.6% determination coefficient (R2), 4.7–27.3% nash-sutcliffe efficient (NSE), 3.7–44.4% relative root mean square error (RRMSE), 3.1–56.2% absolute (MAE), respectively. optimization, MDSL-GWA-PT TMR, median values R2, NSE, RRMSE, MAE ranged 0.907–0.958, 0.887–0.925, 0.083–0.103, 0.115–0.162 mm, In SMR, MDSL-GWA-HS produced estimates, being 0.876, 0.843, 0.112, 0.146 summary, using accessible data which helpful decision-making effective management utilization regional resources.

Language: Английский

Citations

12
Machine-learning-based short-term forecasting of daily precipitation in different climate regions across the contiguous United States DOI
Mohammad Valipour, Helaleh Khoshkam, Sayed M. Bateni

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121907 - 121907

Published: Oct. 2, 2023

Language: Английский

Citations

10
Better with fewer features: climate dynamics estimation for Van Lake basin using feature selection DOI
Önder Çoban, Musa Eşit, Sercan Yalçın

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Language: Английский

Citations

0