Prediction of pH Value by Multi-Classification in the Weizhou Island Area

Huang, Haocai; Feng, Rendong; Zhu, Jiang; Li, Peiliang

doi:10.3390/s19183875

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Using RapidEye imagery, Yigit Avdan et al [60] attempted to develop a model to predict pH by comparing in situ measurements and sensor reflectance values obtained over a lake but were unable to find any correlation among the data. Huang et al [61] developed a method to indirectly assess pH in water bodies through correlation with other water quality parameters such as salinity, temperature, and dissolved oxygen.…”

Section: Phmentioning

confidence: 99%

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Hossain

Mathias

Blanton³

2021

Remote Sensing

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The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.

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Section: Phmentioning

confidence: 99%

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Hossain

Mathias

Blanton³

2021

Remote Sensing

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“…In the learning stage of this paper, the probability distribution θ of the real outputŶ i is denoted aŝ Y [i] , and the expected output Y i is converted to the polynomial distribution vector of one-hot encoding as Y [i] . Next, a value describing the distance between them is given, and the distance sum of all messages is accumulated as the loss function learned [29], which is given by:…”

Section: Learning Stagementioning

confidence: 99%

Vulnerability Mining Method for the Modbus TCP Using an Anti-Sample Fuzzer

Lai

Gao

Liu

2020

Sensors

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Vulnerability mining technology is used for protecting the security of industrial control systems and their network protocols. Traditionally, vulnerability mining methods have the shortcomings of poor vulnerability mining ability and low reception rate. In this study, a test case generation model for vulnerability mining of the Modbus TCP based on an anti-sample algorithm is proposed. Firstly, a recurrent neural network is trained to learn the semantics of the protocol data unit. The softmax function is used to express the probability distribution of data values. Next, the random variable threshold and the maximum probability are compared in the algorithm to determine whether to replace the current data value with the minimum probability data value. Finally, the Modbus application protocol (MBAP) header is completed according to the protocol specification. Experiments using the anti-sample fuzzer show that it not only improves the reception rate of test cases and the ability to exploit vulnerabilities, but also detects vulnerabilities of industrial control protocols more quickly.

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“…The application of deep learning and data-intelligent models has significantly reduced the cost of monitoring and assessment of water quality. Studies conducted include the prediction of pH in water (Egbueri & Agbasi, 2022b ; Huang et al, 2019 ; Son et al, 2021 ; Stackelberg et al, 2020 ), prediction of TDS in water (Egbueri & Agbasi, 2022b ; Jamei et al, 2020 ; Mehrdadi et al, 2012 ; Salmani & Jajaei, 2016 ), prediction of TH in water (Azad et al, 2018 ; Egbueri & Agbasi, 2022b ; Roy & Majumder, 2018 ), prediction of anions in water (Egbueri, 2021 ; Mousavi & Amiri, 2012 ; Wagh et al, 2017b ; Yesilnacar et al, 2008 ; Zare et al, 2011 ), prediction of cations in water (Aghel et al, 2019 ; Bondarev, 2019 ; Katimon et al, 2018 ; Nhantumbo et al, 2018 ; Subba Rao et al, 2022b ), prediction of metals in water (Alizamir & Sobhanardakani, 2017a , 2017b ; Egbueri, 2021 ; Fard et al, 2017 ; Ozel et al, 2020 ; Rooki et al, 2011 ), and prediction of water quality indices (Chia et al, 2022 ; Egbueri, 2022a , 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Agbasi

Egbueri

2023

J. Sediment. Environ.

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Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria, works that have used data-driven algorithms in predicting PTEs in groundwater are scarce. In addition, only a few works have simulated water quality indices using machine learning modelling methods in the region. Therefore, in this study, physicochemical analyses were carried out on groundwater samples in southeastern Nigeria. The laboratory results were used to compute two water quality indices: pollution index of groundwater (PIG) and the water pollution index (WPI), to ascertain groundwater quality. In addition, the physicochemical parameters served as input variables for multiple linear regression (MLR) and artificial neural network (ANN) modelling and prediction of Cr, Fe, Ni, NO 3 − , Pb, Zn, WPI, and PIG. The results of WPI and PIG computation showed that about 30–35% of the groundwater samples were unsuitable for human consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated strong positive prediction accuracies ( R 2 = 0.856–1.000) with low modeling errors. The predictive MLR and ANN models of the PIG and WPI generally outperformed those of the PTEs. The models produced in this study predicted the PTEs better compared to previous studies. Thus, this work provides insights into effective water sustainability, management, and protection.

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Prediction of pH Value by Multi-Classification in the Weizhou Island Area

Cited by 6 publications

References 26 publications

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

Vulnerability Mining Method for the Modbus TCP Using an Anti-Sample Fuzzer

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

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