Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin

“…RF is a combination of multiple prediction trees, which are independent and dependent on the sample. The final prediction result is the average of multiple trees (Sakaa et al, 2022). SVR is a learning machine algorithm based on statistical learning theory and structural risk minimisation principle.…”

Section: Methodsmentioning

confidence: 99%

Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning

Zhang,

Jin

et al. 2024

Ecohydrology

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Chromophoric dissolved organic matter (CDOM) in aquatic environments is an important component of the biogeochemical cycle and carbon cycle. The aim of this study is to investigate the long‐term changes in CDOM in shallow and eutrophic Chaohu Lake, as well as its relationship with climate, environment and social factors. Using long time series Landsat image data and machine learning technology, the spatiotemporal evolution of Chaohu CDOM since 1987 was reconstructed. A total of 180 samples were collected, which were divided into three parts based on regional and hydrological characteristics. The results show that the water quality in different regions were significantly different, and TN may be the key factor driving the change of CDOM in Chaohu Lake. Machine learning algorithms including random forest (RF), support vector regression (SVR), neural network (NN), multimodal deep learning (MDL) model and Extreme Gradient Boosting (XGBoost) were used, among which XGBoost model performed best (R2 = 0.955, mean absolute error [MAE] = 0.024 mg/L, root mean square error [RMSE] = 0.036 mg/L, bias = 1.005) and was used for CDOM spatiotemporal variation retrieval. The change of CDOM was seasonal, highest in August (0.67 m−1) and lowest in December (0.48 m−1), and the western lake is the main source of CDOM. Annual variability of the CDOM indicates that it began to decline after the completion of water pollution control in 2000. Temperature changes were closely related to CDOM (P < 0.01) and agricultural non‐point source pollution plays an important role in Chaohu Lake. This study will provide feasible methods and scientific basis for the long‐term remote sensing supervision of CDOM.

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“…In another study, Bachir Sakaa and his colleagues developed a Random Forest model as well as a Sequential Minimal Optimization-Support Vector Machine method for the determination of water quality in Saf-Saf river [13]. The researchers chose to collect the data from 35 areas in wet and dry seasons in order to gather 70 total samples that make up their dataset.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Quality of Water According to a Random Forest Classifier

Alomani¹,

Alhawiti²,

Alhakamy³

2022

IJACSA

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Potable or drinking water is a daily life necessity for humans. The safety of this water is a concern in many regions around the world, since polluted waters are increasing and causing the spread of disease among populations. Continuous management and evaluation of the water which is meant for drinking is very essential and must be taken seriously. Often, the quality of water is evaluated through regular laboratory testing and analysis which can be tiresome and time consuming. On the other hand, advanced technologies using big data with the help of machine learning can have better results in terms of potability evaluation. For this reason, several studies have been conducted on predicting the quality of water and the several factors and classification that affect the prediction model. In this study, a random forest model was developed using PySpark classification to predict the potability of river water by relying on ten different features: pH, hardness, presence of solids, presence of chloramines, presence of sulfate, conductivity, organic carbon, trihalomethanes, turbidity, and finally potability. In addition, The developed model was able to predict water potability classification with a 1.0 accuracy, and 1.0 F1-score.

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Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin

Cited by 62 publications

References 47 publications

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning

Prediction of Quality of Water According to a Random Forest Classifier

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