River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Journal of environmental chemical engineering, Journal Year: 2020, Volume and Issue: 9(1), P. 104599 - 104599

Published: Oct. 18, 2020

Language: Английский

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Prediction of groundwater quality using efficient machine learning technique

Chemosphere, Journal Year: 2021, Volume and Issue: 276, P. 130265 - 130265

Published: March 18, 2021

Language: Английский

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Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Symmetry, Journal Year: 2020, Volume and Issue: 12(6), P. 1022 - 1022

Published: June 17, 2020

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of Bayes Network (BN), Naïve (NB), Decision Tree (DT), Multivariate Logistic Regression (MLP) machine learning methods for prediction across Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing information from 57 historical fires set nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, distance roads residential areas. Using area under receiver operating characteristic curve (AUC) seven other performance metrics, models were validated terms their to elucidate general behaviors Park predict future fires. Despite few differences between AUC values, BN model with an value 0.96 dominant over predicting second best DT (AUC = 0.94), followed by NB 0.939), MLR 0.937) models. Our robust analysis demonstrated that these are sufficiently response training validation datasets change. Further, results revealed moderate high levels susceptibilities associated ~19% where human activities numerous. resultant maps provide basis developing more efficient fire-fighting strategies reorganizing policies favor sustainable management forest resources.

Language: Английский

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Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Applied Water Science, Journal Year: 2021, Volume and Issue: 11(12)

Published: Nov. 6, 2021

Abstract Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a index (WQI) requires some parameters. Conventionally, WQI computation consumes time and often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), subspace (RSS), additive (AR), neural network (ANN), support vector (SVR), locally weighted linear (LWLR), were employed generate prediction in Illizi region, southeast Algeria. Using best subset regression, 12 different input combinations developed strategy work was based on two scenarios. The first scenario aims reduce consumption computation, where all parameters used as inputs. second intends show variation critical cases when necessary analyses are unavailable, whereas inputs reduced sensitivity analysis. models appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root square (RMSE), relative (RAE), (RRSE). results reveal that TDS TH key drivers influencing study area. comparison performance evaluation metric shows MLR model has higher accuracy compared other terms 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, 3.1708*10–08% for R, MAE, RMSE, RAE, RRSE, respectively. executed less rate by RF 0.9984, 1.9942, 3.2488, 4.693, 5.9642 outcomes paper would be interest planners improving sustainable management plans groundwater resources.

Language: Английский

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Water quality classification using machine learning algorithms

Journal of Water Process Engineering, Journal Year: 2022, Volume and Issue: 48, P. 102920 - 102920

Published: June 18, 2022

Language: Английский

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A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

Water Research, Journal Year: 2022, Volume and Issue: 229, P. 119422 - 119422

Published: Nov. 25, 2022

With the significant increase in WQI applications worldwide and lack of specific application guidelines, accuracy reliability models is a major issue. It has been reported that produce uncertainties during various stages their including: (i) water quality indicator selection, (ii) sub-index (SI) calculation, (iii) weighting (iv) aggregation sub-indices to calculate overall index. This research provides robust statistically sound methodology for assessment model uncertainties. Eight are considered. The Monte Carlo simulation (MCS) technique was applied estimate uncertainty, while Gaussian Process Regression (GPR) algorithm utilised predict at each sampling site. functions were found contribute considerable uncertainty hence affect - they contributed 12.86% 10.27% summer winter applications, respectively. Therefore, selection function needs be made with care. A low less than 1% produced by processes. Significant statistical differences between functions. weighted quadratic mean (WQM) provide plausible coastal waters reduced levels. findings this study also suggest unweighted root means squared (RMS) could potentially used quality. Findings from inform range stakeholders including decision-makers, researchers, agencies responsible monitoring, management.

Language: Английский

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Robust machine learning algorithms for predicting coastal water quality index

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 321, P. 115923 - 115923

Published: Aug. 19, 2022

Coastal water quality assessment is an essential task to keep "good quality" status for living organisms in coastal ecosystems. The Water index (WQI) a widely used tool assess but this technique has received much criticism due the model's reliability and inconsistence. present study recently developed improved WQI model calculating WQIs Cork Harbour. aim of research determine most reliable robust machine learning (ML) algorithm(s) anticipate at each monitoring point instead repeatedly employing SI weight values order reduce uncertainty. In study, we compared eight commonly algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra (ExT), Support Vector Machine (SVM), Linear Regression (LR), Gaussian Naïve Bayes (GNB). For purposes developing prediction models, dataset was divided into two groups: training (70%) testing (30%), whereas models were validated using 10-fold cross-validation method. evaluate models' performance, RMSE, MSE, MAE, R2, PREI metrics study. tree-based DT (RMSE = 0.0, MSE MAE R2 1.0 PERI 0.0) ExT ensemble XGB +0.16 -0.17) RF 2.0, 3.80, 1.10, 0.98, +3.52 -25.38) outperformed other models. results performance indicate that DT, ExT, GXB could be effective, significantly uncertainty predicting WQIs. findings are also useful reducing optimizing WQM-WQI architecture values.

Language: Английский

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Water quality prediction and classification based on principal component regression and gradient boosting classifier approach

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2021, Volume and Issue: 34(8), P. 4773 - 4781

Published: June 14, 2021

Estimating water quality has been one of the significant challenges faced by world in recent decades. This paper presents a prediction model utilizing principal component regression technique. Firstly, index (WQI) is calculated using weighted arithmetic method. Secondly, analysis (PCA) applied to dataset, and most dominant WQI parameters have extracted. Thirdly, predict WQI, different algorithms are used PCA output. Finally, Gradient Boosting Classifier utilized classify status. The proposed system experimentally evaluated on Gulshan Lake-related dataset. results demonstrate 95% accuracy for method 100% classification method, which show credible performance compared with state-of-art models.

Language: Английский

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Groundwater quality assessment using a new integrated-weight water quality index (IWQI) and driver analysis in the Jiaokou Irrigation District, China

Ecotoxicology and Environmental Safety, Journal Year: 2021, Volume and Issue: 212, P. 111992 - 111992

Published: Jan. 30, 2021

Groundwater is an important water resource in arid and semi-arid regions. The impact of human activities on groundwater increasing. After 60 years running, the quality its formation mechanism are imperative questions needed to be answered Jiaokou Irrigation District, Guanzhong Basin, China. In this study, District was assessed by a new integrated-weight index (IWQI), chemistry studied through integrated statistical, geostatistical hydrogeochemical approaches. patterns for average anion cation concentrations were HCO3− > SO42− Cl− NO3− CO32− NO2−, Na+ Mg2+ Ca2+ K+ NH4+, respectively. Statistics showed that major types HCO3-Na, SO4·Cl-Na, Cl·SO4-Na. A (IWQI) proposed based entropy-weighted method CRITIC excellent performance explaining evaluating quality. IWQI results show 65.33% groundwater, mainly distributed central western parts study area, unsuitable drinking. Furthermore, SO42-, HCO3-, Cl-, NO3-, had more effects weathering process affecting area carbonate dissolution, followed silicate evaporite whereas geochemical processes include dissolution precipitation calcite, as well dolomite gypsum (anhydrite). Cation exchange also plays role evolution with long residence time. Anthropogenic included long-term irrigation infiltration excessive use fertilizers. findings can improve understanding driving used reference other similar regions world.

Language: Английский

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Stream water quality prediction using boosted regression tree and random forest models

Stochastic Environmental Research and Risk Assessment, Journal Year: 2022, Volume and Issue: 36(9), P. 2661 - 2680

Published: Jan. 20, 2022

Language: Английский

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