Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches

Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore, this study introduces a novel approach to examine, modeling, and addressing the mo...

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Vydáno v:Water resources management Ročník 39; číslo 3; s. 1073 - 1087
Hlavní autoři: Poursaeid, Mojtaba, Poursaeed, Amir Hossein, Shabanlou, Saeid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.02.2025
Springer Nature B.V
Témata:
Algorithms
Artificial neural networks
Atmospheric Sciences
Civil Engineering
Cleaning
Climate change
Colorado
data collection
Datasets
Dissolved oxygen
Earth and Environmental Science
Earth Sciences
Ensemble learning
Environment
Environmental protection
Geological surveys
Geotechnical Engineering & Applied Earth Sciences
Global climate
hydrogen
Hydrogeology
Hydrology/Water Resources
Indicators
Learning algorithms
Linear programming
Machine learning
Missing data
Neural networks
Nonlinear programming
Optimization
Parameters
Population growth
Regression analysis
River basins
Simulated annealing
surveys
variance
water
Water monitoring
Water quality
Water quality management
Water resources
watersheds
United States > US
Colorado
South Platte river
Water quality
Machine learning
Simulated annealing
Nonlinear programming
Ensemble learning
Water resources
ISSN:0920-4741, 1573-1650
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Shrnutí:Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore, this study introduces a novel approach to examine, modeling, and addressing the monitoring of water quality (WQ) critical scenario related to unexpected extreme variations of crucial indicators (UEVCI). Therefore, this research integrates ensemble machine learning (EML) techniques with Non-linear programming (NLP) and Simulated annealing algorithm (SAA) to develop an optimal weighted ensemble models. New development models were nonlinear-programmed ensemble machine learning (NLEML) and simulated annealing ensemble machine learning (SAEML). Besides, we developed least-squared boosted regression tree (LsBRT), artificial neural network (ANN), and multiple linear regression (MLR) models individually to compare the performance of new ensemble models. The South Platte River Basin in Colorado, USA was the study region. The initial dataset was extracted through the United States Geologic Survey (USGS) from 2023 to 2024. Preprocessing approaches such as cleaning missing data (CMD), cleaning outlier data (COD), and k-fold cross validation (KFCV) with k = 5 were used to prepare the dataset. The final dataset was utilized to examine variations of essential parameters that affect water health and quality, including the power of hydrogen (pH) and dissolved oxygen (DO). The results showed that the NLEML provided the most accurate results in estimating fluctuation of pH parameter with an R 2 coefficient of 0.85. Also, the NLEML estimated the variance of the DO parameter with an R 2 equal to of 0.79, resulting in an outperforming simulation.
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-024-04006-4