Robust machine learning algorithms for predicting coastal water quality index

Uddin, Md Galal; Diganta, Mir Talas Mahammad; Rahman, Azizur; Olbert, Agnieszka Indiana

doi:10.1016/j.jenvman.2022.115923

Cited by 105 publications

(63 citation statements)

References 46 publications

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“…Usually, in ML technique, this approach used to convert various types of variables into uniform scale. In this research, data was standardized using the approach of Uddin et al (2022c). Details of the methodology can be found in Uddin et al (2022c).…”

Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

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

2023

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Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

“…In this research, data was standardized using the approach of Uddin et al (2022c). Details of the methodology can be found in Uddin et al (2022c).…”

Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

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

2023

Self Cite

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“…In particular, the extremely small values in the north and the extremely large values in the south may be overestimated and underestimated to some extent, respectively, due to limited samples of extremely small and large values (compared with the rest of the samples) when training the machine learning model. The overestimation and underestimation issues caused by limited training samples of extreme values are quite common in machine learning (Wu et al, 2022;Li et al, 2020;Uddin et al, 2022;Cho et al, 2020).…”

Section: Overall Accuracymentioning

confidence: 99%

HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Zhang

Luo

Zhao

et al. 2023

Earth Syst. Sci. Data

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Abstract. Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

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“…Recently, several studies have utilised the state-of-the-art WQI for assessing river water quality in India (Chakravarty and Gupta, 2021). In this research, the modified NSF-WQI model was used for the assessment of Mahananda River water quality because recent study have revealed that this modified techniques is effective to assess surface water quality in order to reduce model uncertainty (Uddin et al, 2022a(Uddin et al, , 2022c. In addition, this technique widely used for assessing different dynamics surface waterbodies (de Andrade Costa et al, 2020;Gradilla-Hernández et al, 2020;Matta et al, 2020).…”

Section: Assessing Water Quality Using Modified Nsf-wqi Modelmentioning

confidence: 99%

“…However, this issue is not only impacting developed countries but also developing countries where the unplanned rapid urbanisation and industrialization, and mass population are the key factors that have worsen the surface water bodies especially rivers within urban territory (Singh et al, 2005;Sun et al, 2019;Wu et al, 2018). Although, developed countries have been adopting policies and strategies to envisage this challenge, developing countries are still not equipped with constitutional framework to tackle the imminent consequences from this threat (Uddin et al, 2022c). Therefore, monitoring of water quality holds great importance.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Urban River Water Quality Using Modified NSF Water Quality Index Model at Siliguri City, West Bengal, India

et al. 2022

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Robust machine learning algorithms for predicting coastal water quality index

Cited by 105 publications

References 46 publications

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

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

HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Assessment of Urban River Water Quality Using Modified NSF Water Quality Index Model at Siliguri City, West Bengal, India

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