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

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

Published: Jan. 17, 2023

Language: Английский

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Land use and habitat quality change in the Yellow River Basin: A perspective with different CMIP6-based scenarios and multiple scales

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 345, P. 118729 - 118729

Published: Aug. 3, 2023

Language: Английский

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Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates

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

Published: Jan. 20, 2025

Language: Английский

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Using fluorescence index (FI) of dissolved organic matter (DOM) to identify non-point source pollution: The difference in FI between soil extracts and wastewater reveals the principle

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 862, P. 160848 - 160848

Published: Dec. 14, 2022

Language: Английский

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Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 765 - 765

Published: Feb. 15, 2023

Water resources, land and soil degradation, desertification, agricultural productivity, food security are all adversely influenced by drought. The prediction of meteorological droughts using the standardized precipitation index (SPI) is crucial for water resource management. modeling results SPI at 3, 6, 9, 12 months based on five types machine learning: support vector (SVM), additive regression, bagging, random subspace, forest. After training, testing, cross-validation folds sub-basin 1, concluded that SVM most effective model predicting different (3, 12). Then, SVM, as best model, was applied 2 timescales it achieved satisfactory outcomes. Its performance validated were achieved. suggested performed better than other models estimating drought sub-basins during testing phase. could be used to predict several timescales, choose remedial measures research basin, assist in management sustainable resources.

Language: Английский

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Application of Innovative Machine Learning Techniques for Long-Term Rainfall Prediction

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

Published: Jan. 1, 2023

Language: Английский

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

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

Published: April 1, 2023

Language: Английский

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Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

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

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A Novel Hybrid Algorithms for Groundwater Level Prediction

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

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A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration

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

Published: April 12, 2023

Language: Английский

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