Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches DOI Creative Commons

T. R. Jayashree,

N. V. Subba Reddy, U. Dinesh Acharya

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(3), P. 1013 - 1032

Published: Jan. 27, 2023

Abstract The increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for scheduling management irrigation is thus urgently needed adapt agricultural activities changing climate. accurate estimation reference crop evapotranspiration (ET0), a vital hydrological component balance need, tiresome task if all relevant climatic variables are unavailable. This study investigates potential four ensemble techniques estimating precise values daily ET0 at representative stations 10 agro-climatic zones state Karnataka, India, from 1979 2014. performance these models was evaluated by using several combinations as inputs tenfold cross-validation. outcomes indicated that predictions based on were most comparison with other input combinations. random forest regressor found deliver best among measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, mean absolute 0.011 mm/day). However, it incurred highest computational cost, whereas cost bagging model linear regression lowest. extreme gradient-boosting delivered stable modified training dataset. work here shows can be recommended ET 0 users’ interests.

Language: Английский

An Integrated Statistical-Machine Learning Approach for Runoff Prediction DOI Open Access
Abhinav Kumar Singh, Pankaj Kumar, Rawshan Ali

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(13), P. 8209 - 8209

Published: July 5, 2022

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space time. There is a crucial need for good soil water management system overcome challenges scarcity other natural adverse events like floods landslides, among others. Rainfall–runoff (R-R) modeling an appropriate approach prediction, making it possible take preventive measures avoid damage caused by hazards such as floods. In present study, several data-driven models, namely, multiple linear regression (MLR), adaptive splines (MARS), support vector machine (SVM), random forest (RF), were used rainfall–runoff prediction Gola watershed, located in south-eastern part Uttarakhand. The model analysis was conducted using daily rainfall data 12 years (2009 2020) watershed. first 80% complete train model, remaining 20% testing period. performance models evaluated based on coefficient determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBAIS) indices. addition numerical comparison, evaluated. Their performances graphical plotting, i.e., time-series line diagram, scatter plot, violin relative Taylor diagram (TD). comparison results revealed that four heuristic methods gave higher accuracy than MLR model. Among learning RF (RMSE (m3/s), R2, NSE, PBIAS (%) = 6.31, 0.96, 0.94, −0.20 during training period, respectively, 5.53, 0.95, 0.92, respectively) surpassed MARS, SVM, forecasting all cases studied. outperformed models’ periods. It can be summarized best-in-class delivers strong potential

Language: Английский

Citations

74
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models DOI
Ahmed Elbeltagi,

Chaitanya B. Pande,

Manish Kumar

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(15), P. 43183 - 43202

Published: Jan. 17, 2023

Language: Английский

Citations

74
Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data DOI Open Access
Reham R. Mostafa, Özgür Kişi,

Rana Muhammad Adnan

et al.

Water, Journal Year: 2023, Volume and Issue: 15(3), P. 486 - 486

Published: Jan. 25, 2023

Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts flood hazards. Evapotranspiration, one of the main components hydrological cycle, highly effective in drought monitoring. This study investigates efficiency two machine-learning methods, random vector functional link (RVFL) relevance machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), artificial hummingbird (AHA) modeling ET0 using limited climatic data, minimum temperature, maximum extraterrestrial radiation. The outcomes hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, RVM-QANA models compared single RVFL RVM models. Various input combinations three data split scenarios were employed. results revealed that AHA QANA considerably methods ET0. Considering periodicity component radiation as inputs prediction accuracy applied methods.

Language: Английский

Citations

71
Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index DOI

Chaitanya B. Pande,

Nand Lal Kushwaha, Israel R. Orimoloye

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(3), P. 1367 - 1399

Published: Feb. 1, 2023

Language: Английский

Citations

51
Global-scale water security and desertification management amidst climate change DOI

Shubham Jain,

Aman Srivastava,

Leena Khadke

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Language: Английский

Citations

21
Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates DOI Creative Commons
Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 20, 2025

Language: Английский

Citations

5
Federated learning based reference evapotranspiration estimation for distributed crop fields DOI Creative Commons
Muhammad Tausif, Muhammad Waseem Iqbal, Rab Nawaz Bashir

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0314921 - e0314921

Published: Feb. 5, 2025

Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ET o ). Efforts have been made to simplify the ) estimation using machine learning models. The existing approaches are limited a single specific area. There is need for ET estimations of multiple locations with diverse weather conditions. study intends propose distinct conditions federated approach. Traditional centralized require aggregating all data in one place, which can be problematic due privacy concerns transfer limitations. However, trains models locally combines knowledge, resulting more generalized estimates across different regions. three geographical Pakistan, each conditions, selected implement proposed model from 2012 2022 locations. At location, named Random Forest Regressor (RFR), Support Vector (SVR), Decision Tree (DTR), evaluated local (ET) global model. feature importance-based analysis also performed assess impacts parameters performance at location. evaluation reveals that (RFR) based outperformed other coefficient determination (R 2 = 0.97%, Root Mean Squared Error (RMSE) 0.44, Absolute (MAE) 0.33 mm day −1 , Percentage (MAPE) 8.18%. yields against site. results suggest maximum temperature wind speed most influential factors predictions.

Language: Английский

Citations

2
Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods DOI
Savaş Bayram, Hatice Çıtakoğlu

Environmental Monitoring and Assessment, Journal Year: 2022, Volume and Issue: 195(1)

Published: Nov. 3, 2022

Language: Английский

Citations

60
Pre- and post-dam river water temperature alteration prediction using advanced machine learning models DOI Open Access
Dinesh Kumar Vishwakarma, Rawshan Ali, Shakeel Ahmad Bhat

et al.

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(55), P. 83321 - 83346

Published: June 28, 2022

Language: Английский

Citations

41
Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments DOI Creative Commons
Ahmed Elbeltagi, Aman Srivastava,

Jinsong Deng

et al.

Agricultural Water Management, Journal Year: 2023, Volume and Issue: 283, P. 108302 - 108302

Published: April 14, 2023

Precise evapotranspiration (ET) estimation is critical for agricultural water management, particularly in water-stressed developing countries. Vapor Pressure Deficit one of the ET parameters that has a significant impact on its calculation (VPD). This paper forecasts VPD using ensemble learning-based modeling eight different regions (Dakahliyah, Gharbiyah, Kafr Elsheikh, Dumyat, Port Said, Ismailia, Sharqiyah, and Qalubiyah) Egypt. In this study, six machine learning algorithms were used: Linear Regression (LR), Additive regression trees (ART), Random SubSpace (RSS), Forest (RF), Reduced Error Pruning Tree (REPTree), Quinlan's M5 algorithm (M5P). Monthly vapor pressure data obtained from Japanese 55-year Reanalysis JRA-55 1958 to 2021. The dateset been divided into two segments: training stage (1958–2005) testing (2006–2021). Five statistical measures used evaluate model performances: Correlation Coefficient (CC), Mean Absolute (MAE), Root Square (RMSE), Relative absolute error (RAE), Squared (RRSE), across both stages. RF outperformed rest models [CC = 0.9694; MAE 0.0967; RMSE 0.1252; RAE (%) 21.7297 RRSE 24.0356], followed closely by REPTree RSS models. On other hand, M5P performance remained moderate LR AR worst. During stage, terms (which statistic), study recommended future hydro-climatological studies general, deficit prediction particular. enables magnitudes be predicted, alerting authorities administrators involved focus their policy-making more specific pathways toward climate adaptation.

Language: Английский

Citations

41