Assessing and forecasting water quality in the Danube River by using neural network approaches

Georgescu, Puiu-Lucian; Moldovanu, Simona; Iticescu, Cătălina; Călmuc, Mădălina; Calmuc, Valentina Andreea; Topa, Catalina; Moraru, Luminița

doi:10.1016/j.scitotenv.2023.162998

Cited by 41 publications

(7 citation statements)

References 54 publications

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“…The Figure 14 shows the ranking of the feature importance provided by RF 59 . It represents the raking of the feature importance for the diffuse cases, CCA, and dense-core cases for different thresholds.…”

Section: Resultsmentioning

confidence: 99%

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Alzheimer’s disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm

Amin,

Elgammal,

Zahran

et al. 2023

Sci Rep

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Alzheimer’s disease (AD) is a physical illness, which damages a person’s brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.

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Section: Resultsmentioning

confidence: 99%

“…In this study, there are three classes ( m = 3) of Aβ plaques as diffuse, CAA, and dense-core. The RF optimized hyperparameters 59 can be listed in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Alzheimer’s disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm

Amin,

Elgammal,

Zahran

et al. 2023

Sci Rep

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“…Machine learning techniques such as RF and BPNN are effective in forecasting outcomes for regression [66,67] and classification [68,69] tasks. RF and BPNN can capture complex nonlinear relationships [70] between input features and target variables. This is accompanied by merits such as interpretability, the ability to handle high-dimensional data and feature interactions, and resistance to overfitting.…”

Section: Introductionmentioning

confidence: 99%

Behavior Prediction and Inverse Design for Self-Rotating Skipping Ropes Based on Random Forest and Neural Network

Qiu,

Wu,

Dai

et al. 2024

Mathematics

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Self-oscillatory systems have great utility in energy harvesting, engines, and actuators due to their ability to convert ambient energy directly into mechanical work. This characteristic makes their design and implementation highly valuable. Due to the complexity of the motion process and the simultaneous influence of multiple parameters, computing self-oscillatory systems proves to be challenging, especially when conducting inverse parameter design. To simplify the computational process, a combined approach o0f Random Forest (RF) and Backpropagation Neural Network (BPNN) algorithms is employed. The example used is a self-rotating skipping rope made of liquid crystal elastomer (LCE) fiber and a mass block under illumination. Numerically solving the governing equations yields precise solutions for the rotation frequency of the LCE skipping rope under various system parameters. A database containing 138,240 sets of parameter conditions and their corresponding rotation frequencies is constructed to train the RF and BPNN models. The training outcomes indicate that RF and BPNN can accurately predict the self-rotating skipping rope frequency under various parameters, demonstrating high stability and computational efficiency. This approach allows us to discover the influences of distinct parameters on the rotation frequency as well. Moreover, it is capable of inverse design, meaning it can derive the corresponding desired parameter combination from a given rotation frequency. Through this study, a deeper understanding of the dynamic behavior of self-oscillatory systems is achieved, offering a new approach and theoretical foundation for their implementation and construction.

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“…XGBoost was identified as the superior classifier based on model validation results. Georgescu et al (2023), aims to forecast Water Quality Index (WQI) time series data using Cascade-forward network (CFN) models, with Radial Basis Function Network (RBF) as a benchmark. Using 19 initial water quality features, CFN models, refined by Random Forest (RF) algorithm, outperform RBF models.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Machine Learning Approaches in Predicting Albanian Shkumbini River's Waters Using Water Quality Index Model

Basha,

Shyti,

Bekteshi

2024

Journal of Environmental Engineering and Landscape Management

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A common technique for assessing the overall water quality state of surface water and groundwater systems globally is the water quality index (WQI) method. The aim of the research is to use four machine learning classifier algorithms: Gradient boosting, Naive Bayes, Random Forest, and K-Nearest Neighbour to determine which model was most effective at forecasting the various water quality index and classes of the Albanian Shkumbini River. The analysis was performed on the data collected during a 4-year period, in six monitoring points, for nine parameters. The predictive accuracy of the models, XGBoost, Random Forest, K-Nearest Neighbour, and Naive Bayes, was determined to be 98.61%, 94.44%, 91.22%, and 94.45%, respectively. Notably, the XGBoost algorithm demonstrated superior performance in terms of F1 score, sensitivity, and prediction accuracy, the lowest errors during both learning (RMSE = 2.1, MSE = 9.8, MAE = 1.13) and evaluating (RMSE = 0.0, MSE = 0.01, MAE = 0.01) stages. The findings highlighted that Biochemical oxygen demand (BOD), Bicarbonate (HCO3), and Total Phosphor had the most positive impact on the Shkumbini River’s water quality. Additionally, a statistically significant, strong positive correlation (r = 0.85) was identified between BOD and WQI, emphasizing its crucial role in influencing water quality in the Shkumbini River.

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Assessing and forecasting water quality in the Danube River by using neural network approaches

Cited by 41 publications

References 54 publications

Alzheimer’s disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm

Alzheimer’s disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm

Behavior Prediction and Inverse Design for Self-Rotating Skipping Ropes Based on Random Forest and Neural Network

Evaluating the Performance of Machine Learning Approaches in Predicting Albanian Shkumbini River's Waters Using Water Quality Index Model

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