A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102500 - 102500

Published: Jan. 28, 2024

The importance of water quality models has increased as their inputs are critical to the development risk assessment framework for environmental management and monitoring rivers. However, with advent a plethora recent advances in ML algorithms better predictions possible. This study proposes causal effect model by considering climatological such temperature precipitation along geospatial information related agricultural land use factor (ALUF), forest (FLUF), grassland usage (GLUF), shrub (SLUF), urban (ULUF). All these factors included input data, whereas four Stream Water Quality parameters (SWQPs) Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, Dissolved (DO) from 2019 2021 taken outputs predict Godavari River Basin quality. In preliminary investigation, out SWQPs, nitrate's coefficient variation (CV) is high, revealing close association climate practices across sampling stations. authors' earlier study, using single-layer Feed-Forward Neural Network (FFNN) showed improved performance predicting cause linked metrics. To achieve prediction, stacked ANN meta-model nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive (ADB), Decision Tree (DT), Highest (HGB), Light Method (LGBM), (GB), were compared this study. According study's findings, outperformed stand-alone FFNN same dataset superior predictive capabilities terms accuracy forecasting variable interest. For instance, during testing, determination (R2) (BOD) 0.72 0.87. Furthermore, Artificial (ANN) meta that was reinforced (ET) base performed than individual (from R2 = 0.87 0.91 BOD testing). By new framework, effort hyperparameter tuning can be minimized.

Language: Английский

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Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102615 - 102615

Published: April 28, 2024

Language: Английский

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Enhancing phosphorus source apportionment in watersheds through species-specific analysis

Water Research, Journal Year: 2024, Volume and Issue: 253, P. 121262 - 121262

Published: Feb. 7, 2024

Language: Английский

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Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101300 - 101300

Published: July 27, 2024

Language: Английский

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Sustainable land use scenarios generated by optimizing ecosystem distribution based on temporal and spatial patterns of ecosystem services in the southern China hilly region

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102275 - 102275

Published: Aug. 29, 2023

Language: Английский

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Towards a generic model evaluation metric for non-normally distributed measurements in water quality and ecosystem models

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102470 - 102470

Published: Jan. 14, 2024

Model evaluation is a crucial process in model development and quantified metrics play pivotal role calibration validation. However, current water quality ecosystem models, conventional are derived from hydrological models often overlook the non-normal distribution of ecological state variables. In this study, we proposed series that consider distributions by modifying components Kling-Gupta efficiency. Subsequently, tested existing newly using four different synthetic datasets actual simulation case total phosphorus, chlorophyll a, dissolved oxygen concentrations. The results demonstrate metric, Fu-Zhang efficiency, which replaces ratio standard deviations with interquartile ranges to measure dispersion similarity, more suitable for evaluating lots outliers measurements. Our study reveals hidden perils integrated calls comprehensive feasible quantitative frameworks drive next paradigm shift.

Language: Английский

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Global soil respiration predictions with associated uncertainties from different spatio-temporal data subsets

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102777 - 102777

Published: Aug. 23, 2024

Soil respiration (Rs), the second-largest flux in global carbon cycle, is a crucial but uncertain component. To improve understanding of Rs, we constructed single models, and specific models classified by climate type, land cover year data record, elevation range using random forest algorithm to predict Rs values explore associated uncertainty models. The results showed similar overall predictive performance for with an R-squared value greater than 0.63; however, significant differences were observed compared estimate (23 Pg C). All estimated larger model, mainly owing imbalances sample on which prediction based. One exception this result estimates smaller 2020 (95.1 Overall, model closer those obtained temperate zones training distribution, resulted other classification-specific Prediction observations before 2000 tend underestimate Rs. However, use proved helpful addressing persistent temporal spatial sampling. Expanding coverage records both temporally spatially updating database promptly would estimation accuracy while enhancing budget feedback soil regard warming.

Language: Английский

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Forecasting biochemical oxygen demand (BOD) in River Ganga: a case study employing supervised machine learning and ANN techniques

Sustainable Water Resources Management, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 16, 2025

Language: Английский

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Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(5)

Published: April 23, 2025

Language: Английский

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Evaluating the Effects of Climate-Induced Heatwaves on the Thermal Dynamics and Water Quality of a Deep Reservoir

Published: Jan. 1, 2025

Language: Английский

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