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

A review of the Artificial Intelligence (AI) based techniques for estimating reference evapotranspiration: Current trends and future perspectives DOI
Pooja Goyal, Sunil Kumar, Rakesh Sharda

et al.

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 209, P. 107836 - 107836

Published: April 28, 2023

Language: Английский

Citations

41
Application of Innovative Machine Learning Techniques for Long-Term Rainfall Prediction DOI
Suman Markuna, Pankaj Kumar, Rawshan Ali

et al.

Pure and Applied Geophysics, Journal Year: 2023, Volume and Issue: 180(1), P. 335 - 363

Published: Jan. 1, 2023

Language: Английский

Citations

38
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing DOI
M. J. Jiménez-Navarro, M. Martínez-Ballesteros, Isabel Sofía Brito

et al.

Published: March 27, 2023

The year 2022 was the driest in Portugal since 1931 with 97% of territory severe drought.Water is especially important for agricultural sector Portugal, as it represents 78% total consumption according to Water Footprint report published 2010.Reference evapotranspiration essential due its importance optimal irrigation planning that reduces water consumption.This study analyzes and proposes a framework forecast daily reference at eight stations from 2012 without relying on public meteorological forecasts.The data include obtained sensors included stations.The goal perform multi-horizon forecasting using multiple related covariates.The combines processing analysis several state-of-the-art methods including classical, linear, tree-based, artificial neural network ensembles.Then, an ensemble all trained models proposed recent bioinspired metaheuristic named Coronavirus Optimization Algorithm weight predictions.The results terms MAE MSE are reported, indicating our approach achieved 0.658.

Language: Английский

Citations

38
Combination of discretization regression with data-driven algorithms for modeling irrigation water quality indices DOI

Dimple Dimple,

Pradeep Kumar Singh, Jitendra Rajput

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102093 - 102093

Published: April 1, 2023

Language: Английский

Citations

34
Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test DOI Creative Commons
Dinesh Kumar Vishwakarma, Alban Kuriqi, Salwan Ali Abed

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(5), P. e16290 - e16290

Published: May 1, 2023

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable fundamental crucial component water resource system engineering. Since continuous measurement often impossible, relationship generally used natural streams to estimate discharge. This paper aims optimize using generalized reduced gradient (GRG) solver test accuracy applicability hybridized linear regression (LR) with other machine learning techniques, namely, regression-random subspace (LR-RSS), regression-reduced error pruning tree (LR-REPTree), regression-support vector (LR-SVM) regression-M5 pruned (LR-M5P) models. An application these hybrid models was performed modeling Gaula Barrage problem. For this, 12-year historical data were collected analyzed. The daily flow (m3/s) stage (m) from during monsoon season, i.e., June October only 03/06/2007 31/10/2018, for discharge simulation. best suitable combination input variables LR, LR-RSS, LR-REPTree, LR-SVM, LR-M5P identified decided gamma test. GRG-based equations found be as effective more accurate conventional equations. outcomes GRG, compared observed values based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index Agreement (d), Kling-Gupta (KGE), mean absolute (MAE), bias (MBE), relative percent (RE), root square (RMSE) Pearson correlation (PCC) determination (R2). LR-REPTree (combination 1: NSE = 0.993, d 0.998, KGE 0.987, PCC(r) 0.997, R2 0.994 minimum value RMSE 0.109, MAE 0.041, MBE −0.010 RE −0.1%; 2; 0.941, 0.984, 0. 923, 973, 947 331, 0.143, −0.089 −0.9%) superior all combinations testing period. It also noticed that performance alone LR its (i.e., LR-M5P) better than curve, including GRG method.

Language: Английский

Citations

31
A Novel Hybrid Algorithms for Groundwater Level Prediction DOI
Mohsen Saroughi, Ehsan Mirzania, Dinesh Kumar Vishwakarma

et al.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2023, Volume and Issue: 47(5), P. 3147 - 3164

Published: March 9, 2023

Language: Английский

Citations

30
A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration DOI
Ehsan Mirzania, Dinesh Kumar Vishwakarma, Quynh-Anh Thi Bui

et al.

Arabian Journal of Geosciences, Journal Year: 2023, Volume and Issue: 16(5)

Published: April 12, 2023

Language: Английский

Citations

25
Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021 DOI
Ahmed Elbeltagi, Aman Srivastava, Penghan Li

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 345, P. 118697 - 118697

Published: Sept. 7, 2023

Language: Английский

Citations

25
Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions DOI
Dinesh Kumar Vishwakarma, Pankaj Kumar, Krishna Kumar Yadav

et al.

Pure and Applied Geophysics, Journal Year: 2024, Volume and Issue: 181(2), P. 719 - 747

Published: Feb. 1, 2024

Language: Английский

Citations

15
Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand DOI

Paramjeet Singh Tulla,

Pravendra Kumar,

Dinesh Kumar Vishwakarma

et al.

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(5), P. 4023 - 4047

Published: Feb. 10, 2024

Language: Английский

Citations

12