Selection of the best machine learning method for estimation of concentration of different water quality parameters

Adib, Arash; Farajpanah, Hiwa; Shoushtari, M Mahmoudian; Lotfirad, Morteza; Saeedpanah, Iraj; Sasani, Hossein

doi:10.1007/s40899-022-00765-3

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Moreover, eutrophication of water sources, characterized by excessive growth of algae, can lead to the production of algal toxins. Drinking water contaminated with algal toxins can cause gastrointestinal diseases and even have teratogenic and mutagenic effects (Arash et al 2022;Hamza et al 2023). The impact of water pollution in water sources on human health is immense, underscoring the crucial importance of monitoring and early warning research on water source environments.…”

Section: Introductionmentioning

confidence: 99%

The eutrophication-related index of drinking water sources based on the oxidation-reduction potential

Zhang,

Zhu,

Liu

et al. 2024

BioRes

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Eutrophication caused by excessive nitrogen and phosphorus is an important factor affecting water quality in drinking water sources. Convenient monitoring of eutrophication in water bodies can reduce the use of pesticides and reduce energy consumption, helping to promote ecological and economic development. This study examined the relationship between water eutrophication and the oxidation-reduction potential (ORP). The results showed that various parameters related to eutrophication, such as ammonia nitrogen, total nitrogen, total phosphorus, chlorophyll-a, and cyanobacteria, had correlations with ORP. There is a close relationship between eutrophication and the concentration of cyanobacteria. When cyanobacteria blooms occur in the drinking water source, it may contaminate the drinking water. Because the conventional eutrophication index does not include the concentration of cyanobacteria, principal component analysis (PCA) was utilized to comprehensively analyze these eutrophication-related parameters and obtain the eutrophication-related index, with the cumulative contribution of principal components reaching 81.8%. Different mathematical methods such as neural network model and mathematical fitting were used to study the relationship between ORP and the eutrophication-related index. A three-segment relationship between the ORP and the index was established. This three-stage relationship was confirmed in different reservoirs.

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Section: Introductionmentioning

confidence: 99%

The eutrophication-related index of drinking water sources based on the oxidation-reduction potential

Zhang,

Zhu,

Liu

et al. 2024

BioRes

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Regional flood frequency analysis using data-driven models (M5, random forest, and ANFIS) and a multivariate regression method in ungauged catchments

Esmaeili-Gisavandani

Zarei

Tehrani³

2023

Appl Water Sci

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Flooding is recognized worldwide joined of the most expensive natural hazards. To adopt proper structural and nonstructural measurements for controlling and mitigating the rising flood risk, the availability of streamflow values along a river is essential. This raises concerns in the hydrological assessment of poorly gauged or ungauged catchments. In this regard, several flood frequency analysis approaches have been conducted in the literature including index flow method (IFM), square grids method (SGM), hybrid method (HM), as well as the conventional multivariate regression method (MRM). While these approaches are often based on assumptions that simplify the complex nature of the hydrological system, they might not be able to address uncertainties associated with the complexity of the system. One of the powerful tools to deal with this issue is data-driven model that can be easily adopted in complex systems. The objective of this research is to utilize three different data-driven models: random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and M5 decision tree algorithm to predict peak flow associated with various return periods in ungauged catchments. Results from each data-driven model were assessed and compared with the conventional multivariate regression method. Results revealed all the three data-driven models performed better than the multivariate regression method. Among them, the RF model not only demonstrated the superior performance of peak flow prediction compared to the other algorithms but also provided insight into the complexity of the system through delivering a mathematical formulation.

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Support vector machine (SVM) model development for prediction of fecal coliform of Upper Green River Watershed, Kentucky, USA

Talnikar,

Anmala,

Venkateswarlu

et al. 2024

Sustain. Water Resour. Manag.

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Selection of the best machine learning method for estimation of concentration of different water quality parameters

Cited by 4 publications

References 46 publications

The eutrophication-related index of drinking water sources based on the oxidation-reduction potential

The eutrophication-related index of drinking water sources based on the oxidation-reduction potential

Regional flood frequency analysis using data-driven models (M5, random forest, and ANFIS) and a multivariate regression method in ungauged catchments

Support vector machine (SVM) model development for prediction of fecal coliform of Upper Green River Watershed, Kentucky, USA

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