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Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions.

ABSTRACT: In this article the data of the groundwater quality of Aras catchment area were investigated for estimating the sodium absorption ratio (SAR) in the years 2010-2014. The artificial neural network (ANN) is defined as a system of processor elements, called neurons, which create a network by a set of weights. In the present data article, a 3-layer MLP neural network including a hidden layer, an input layer and an output layer had been designed. The number of neurons in the input and output layers of network was considered to be 4 and 1, respectively, due to having four input variables (including: pH, sulfate, chloride and electrical conductivity (EC)) and only one output variable (sodium absorption ratio). The impact of pH, sulfate, chloride and EC were estimated to be 11.34%, 72.22%, 94% and 91%, respectively. ANN and multiple linear regression methods were used to estimate the rate of sodium absorption ratio of groundwater resources of the Aras catchment area. The data of both methods were compared with the model?s performance evaluation criteria, namely root mean square error (RMSE), mean absolute error (%) and correlation coefficient. The data showed that ANN is a helpful and exact tool for predicting the amount SAR in groundwater resources of Aras catchment area and these results are not comparable with the results of multiple linear regressions.

SUBMITTER: Radfard M

PROVIDER: S-EPMC6153356 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions.

Radfard Majid M Soleimani Hamed H Nabavi Samira S Hashemzadeh Bayram B Akbari Hesam H Akbari Hamed H Adibzadeh Amir A

Data in brief 20180905

In this article the data of the groundwater quality of Aras catchment area were investigated for estimating the sodium absorption ratio (SAR) in the years 2010-2014. The artificial neural network (ANN) is defined as a system of processor elements, called neurons, which create a network by a set of weights. In the present data article, a 3-layer MLP neural network including a hidden layer, an input layer and an output layer had been designed. The number of neurons in the input and output layers o ...[more]

PMID: 30258950

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Project description:MotivationSystems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology.ResultsHere we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability.Availability and implementationThe R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer .Contactsteven.kleinstein@yale.edu or stefan.avey@yale.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

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Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions.

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Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions.

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