Empirical Models to Predict Disinfection By-Products (DBPs) in Drinking Water: An Updated Review

Sadiq, Rehan; Rodriguez, Manuel J.; Mian, Haroon R.

doi:10.1016/b978-0-12-409548-9.11193-5

Cited by 44 publications

(27 citation statements)

References 118 publications

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“…A distinct characteristic of ANNs is that they can develop relationships between dependent and independent variables, and thus extract useful information from representative datasets. ANN models can offer several benefits, such as their ability to handle noisy data [ 86 ], a situation that is very common in agricultural measurements. Among the most popular ANNs are the Deep Neural Networks (DNNs), which utilize multiple hidden layers between input and output layers.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

424

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Resultsmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

424

146

View full text Add to dashboard Cite

show abstract

“…Without making any assumptions regarding a formal representation of the events, the connections between independent and dependent variables can be established. ANN models have several advantages over regression-based models, such as the ability to handle noisy data (Sadiq et al, 2019).…”

Section: Ann and Dann In Medical Diagnosismentioning

confidence: 99%

“…For infrequent or extreme events, where data is inadequate to train the model, ANNs cannot be used. Human expertise cannot be used as a replacement for quantitative evidence in ANNs (Sadiq et al, 2019).…”

Section: Ann and Dann In Medical Diagnosismentioning

confidence: 99%

Adoption of machine learning for medical diagnosis

Shaheen¹

2021

Preprint

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The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.

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“…As an alternative to measuring THMs, multiple prediction models have been developed. These models can be generated from laboratory or eld data by collecting samples at the treatment plant and/or distribution network (Sadiq et al 2019). For the rst case they have the advantage that many variables can be controlled, however, it does not contemplate certain aspects that occur on a real scale (Chowdhury et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Modeling the formation of trihalomethanes in rural and semi-urban drinking water distribution networks of Costa Rica

Kelly-Coto

Jiménez

Mora-Campos

et al. 2022

Environ Sci Pollut Res

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Chlorination is one of the most important stages in the treatment of drinking water due to its effectiveness in the inactivation of pathogenic organisms. However, the reaction between chlorine and natural organic matter (NOM) generates harmful disinfection by-products (DBPs), such as trihalomethanes (THMs). In this research, drinking water quality data was collected from the distribution networks of 19 rural and semi urban systems that use water sources as: springs, surfaces, and a mixture of both, in three provinces of the Paci c slope of Costa Rica during April 2018 to September 2019. Twelve models were developed from four data sets: all water sources, spring, surface, and mixture of spring and surface waters. Linear, logarithmic, and exponential multivariate regression models were developed for each data set to predict the concentration of total trihalomethanes (TTHMs) in the distribution networks. Concentrations of TTHMs were found between < 0.20 to 91.31 µg/L, with chloroform being the dominant species accounting for 62% of TTHMs on average. Turbidity, free residual chlorine, total organic carbon (TOC), dissolved organic carbon (DOC) and ultraviolet absorbance at 254 nm (UV 254 ) showed a signi cant correlation with TTHMs. In all the data sets the linear models presented the best goodness-of-t and were moderately robust. Four models, the best of each data set, were validated with data from the same systems, and, according to the criteria of R 2 , SE, MSE and MAE, spring water and mixed spring/surface water models showed a satisfactory level of explanation of the variability of the data. Moreover, the models seem to better predict TTHMs concentrations below 30 µg/L. These models were satisfactory and could be useful for decision-making in drinking water supply systems and be considered in possible modi cations in current legislation. prediction in Costa Rica, and to the best of the authors knowledge in the Central American and Caribbean region. Furthermore, this study was focused in rural and semi urban areas, where no studies was found in the literature.In Costa Rica, 93% of the population received drinking water in 2019 (PEN and CONARE 2020). Moreover, in the same year, 19.4% of homes in rural and semi-urban areas were supplied with water by local Associations Administrators of Aqueduct and Sewerage Systems, (ASADAs in Spanish) (Sánchez-Hernández 2019). In addition, in 2016, 14.3% of the population was supplied by 24 municipalities and the rest by duly organized public companies (AyA 2016). The main water sources used are groundwater, springs, surface water and the mixture of the two latter ones, moreover, in all cases chlorine disinfection is the method used (Arellano-Hartig et al. 2020). In general, due to economic and analytical capacity limitations, monitoring of THMs is scarce, mainly at the ASADAs and municipal level. Thus, the objective of this study was to develop a series of prediction models of TTHMs in the distribution systems of rural and semi-urban areas supplied by springs, surface water and t...

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Empirical Models to Predict Disinfection By-Products (DBPs) in Drinking Water: An Updated Review

Cited by 44 publications

References 118 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Adoption of machine learning for medical diagnosis

Modeling the formation of trihalomethanes in rural and semi-urban drinking water distribution networks of Costa Rica

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