Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR)

Sepahvand, Alireza; Singh, Balraj; Sihag, Parveen; Samani, Aliakbar Nazari; Hasan, Ahmadi; Nia, Sadat Fiz

doi:10.1080/09715010.2019.1595185

Cited by 35 publications

(20 citation statements)

References 33 publications

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“…The study by Sepahvand et al [ 48 ] focused on the performance of four AI models in predicting SAR; the evaluated models are M5P model tree, RF, implementing bagging algorithm on M5P, and group method for data handling (GMDH). From the results of the study, bagging M5P model tree model achieved higher accuracy in SAR prediction compare to the rest of the models in a given study area.…”

Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

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Section: Introductionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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“…The AI techniques have been applied and approved recently as an appropriate tool to model composite nonlinear phenomena in water bodies system and hydrology. Recent investigations have used the capabilities of the artificial neural network (ANN) in modeling water resource variables (Heddam & Kisi, 2018;Malik et al, 2019;Sepahvand et al, 2019). Another research by (Alizadeh et al, 2018) have been used the machine learning to study the effect of river flow on the quality of estuarine and coastal water.…”

Section: Introductionmentioning

confidence: 99%

Efficient river water quality index prediction considering minimal number of inputs variables

Othman

Alaaeldin

Seyam

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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Water Quality Index (WQI) is the most common determinant of the quality of the stream-flow. According to the Department of Environment (DOE, Malaysia), WQI is chiefly affected by six factors, which are, chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), suspended solids (SS),-potential for hydrogen (pH), and ammoniacal nitrogen (AN). In fact, understanding the interrelationships between these variables and WQI can improve predicting the WQI for better water resources management. The aim of this study is to create an input approach using ANNs (Artificial Neural Networks) to compute the WQI from input parameters instead of using the indices of the parameters when one of the parameters is absent. The data are collected from the nine water quality monitoring stations at the Klang River basin, Malaysia. In addition, comprehensive sensitivity analysis has been carried out to identify the most influential input parameters. The model is based on the frequency distribution of the significant factors showed exceptional ability to replicate the WQI and attained very high correlation (98.78%). Furthermore, the sensitivity analysis showed that the most influential parameter that affects WQI is DO, while pH is the least one. Additionally, the performance of models shows that the missing DO values caused deterioration in the accuracy.

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“…GMDH was developed to solve the problems of predication, complex system, and optimization by using a nonlinear regression algorithm. GMDH structure is classified as a self-organizing polynomial neural network's method [44]. GMDH is a specific type of supervised artificial neural network.…”

Section: Introduction To the Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

et al. 2021

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Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.

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Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR)

Cited by 35 publications

References 33 publications

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Efficient river water quality index prediction considering minimal number of inputs variables

On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

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