2021
DOI: 10.1002/wer.1642
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Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

Abstract: Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of strea… Show more

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Cited by 39 publications
(21 citation statements)
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“…Because of its quick learning speed, good generalization capabilities, and ease of application, ELM has stimulated interest of a wide range of sectors. This model has been applied in palmprint recognition, 23 medical treatment, [24][25][26][27][28] motion image classification, 29 communication networks, 30 environmental management, 31 water quality detection, 32 and…”
Section: Discussionmentioning
confidence: 99%
“…Because of its quick learning speed, good generalization capabilities, and ease of application, ELM has stimulated interest of a wide range of sectors. This model has been applied in palmprint recognition, 23 medical treatment, [24][25][26][27][28] motion image classification, 29 communication networks, 30 environmental management, 31 water quality detection, 32 and…”
Section: Discussionmentioning
confidence: 99%
“…Using the combined data from the 13 monitoring stations, we performed basic statistical analysis to visualize the water quality data of the river system, and the complete dataset provided an insight into the pollution status of the rivers near Dhaka City. Other studies (Anmala & Turuganti, 2021; Bourel et al, 2021; Imani et al, 2021; Ma et al, 2020) also used water quality data collected from several monitoring stations that were scattered throughout the study area and complied them together to develop ML prediction models.…”
Section: Methodsmentioning
confidence: 99%
“…In river water systems, Haghiabi et al (2018), Hayder et al (2020), and Sarkar and Pandey (2015 developed and investigated the performances of different ML models for predicting water quality parameters. Venkateswarlu et al (2020) and Anmala and Turuganti (2021) developed water quality prediction models to determine the impacts of climate and land use on stream water quality using different ML methods such as PCA, CCA, ANN, decision tree (DT), and extreme machine learning (EML) algorithm. Other studies (Ahmed et al, 2019;Babbar & Babbar, 2017;Bui et al, 2020;Chen et al, 2020;Hameed et al, 2017;Sakizadeh, 2016;Wang et al, 2017) used ML techniques to predict water quality based on water quality index (WQI).…”
Section: Introductionmentioning
confidence: 99%
“…Under the same conditions, the 2DD-CNN is compared with other models. The comparison model includes a CNN, an LSTM model, CNN-LSTM, a back Propagation Neuron Network (BP) (Zhang and Lou, 2021), a decision tree (DT) (Anmala and Turuganti, 2021), a RF (Karijadi and Chou, 2022), dynamic evolving neural fuzzy inference system (DENFIS) (Adnan et al, 2019), a group method of data handling (GMDH) neural networks (Adnan et al, 2020) a hybrid model based on long short-term memory neural network and ant lion optimizer (LSTM-ALO) (Yuan et al, 2018), a hybrid model based on an optimally pruned extreme learning machine (OP-ELM) and a hybrid model based on the least squares support vector machine and gravitational search algorithm (LSSVM-GSA) (Zeng et al, 2021).…”
Section: Single Ranch Prediction Evaluation With Other Prediction Modelsmentioning
confidence: 99%