Comparative approach for soil quality index based on spatial multi-criteria analysis and artificial neural network

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Sena Pacci ◽  
Nursaç Serda Kaya ◽  
İnci Demirağ Turan ◽  
Mehmet Serhat Odabas ◽  
Orhan Dengiz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Quality ◽  
Quality Index ◽  
Comparative Approach ◽  
Soil Quality Index ◽  
Artificial Neural ◽  
Multi Criteria Analysis

scholarly journals Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5875
Author(s):  
Monika Kulisz ◽  
Justyna Kujawska ◽  
Bartosz Przysucha ◽  
Wojciech Cel
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Quality Index ◽  
Quality Index ◽  
Network Models ◽  
Multiple Regression Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.

scholarly journals Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis

MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 1021-1029 ◽  
Author(s):  
Majid RadFard ◽  
Mozhgan Seif ◽  
Amir Hossein Ghazizadeh Hashemi ◽  
Ahmad Zarei ◽  
Mohammad Hossein Saghi ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Water Resources ◽  
Water Quality Index ◽  
Quality Index ◽  
Drinking Water Quality ◽  
Arc Gis ◽  
Artificial Neural

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

foresight ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alireza Sedighi Fard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
World Health ◽  
Comparative Approach ◽  
Data Set ◽  
Current Time ◽  
Content Type ◽  
Artificial Neural

Purpose This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future. Design/methodology/approach The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19. Findings The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose. Originality/value Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

scholarly journals Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
WesamEldin I. A. Saber ◽  
Noura El-Ahmady El-Naggar ◽  
Mohammed S. El-Hersh ◽  
Ayman Y. El-khateeb ◽  
Ashraf Elsayed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Central Composite Design ◽  
Immobilized Cells ◽  
Comparative Approach ◽  
Bacterial Cells ◽  
Ann Model ◽  
Biosorption Process ◽  
Artificial Neural ◽  
Central Composite

AbstractHeavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and severe health concerns. The current study is of supreme priority because there is no previous work that dealt with the modeling of the optimization of the biosorption process by the immobilized cells. The significant parameters (immobilized bacterial cells, contact time, and initial Cr6+ concentrations), affecting Cr6+ biosorption by immobilized Pseudomonas alcaliphila, was verified, using the Plackett–Burman matrix. For modeling the maximization of Cr6+ biosorption, a comparative approach was created between rotatable central composite design (RCCD) and artificial neural network (ANN) to choose the most fitted model that accurately predicts Cr6+ removal percent by immobilized cells. Experimental data of RCCD was employed to train a feed-forward multilayered perceptron ANN algorithm. The predictive competence of the ANN model was more precise than RCCD when forecasting the best appropriate wastewater treatment. After the biosorption, a new shiny large particle on the bead surface was noticed by the scanning electron microscopy, and an additional peak of Cr6+ was appeared by the energy dispersive X-ray analysis, confirming the role of the immobilized bacteria in the biosorption of Cr6+ ions.

Application of Artificial Neural Network to Predict the Water Quality Index of Brahmani River Basin Odisha, India

2018 ◽  
Vol 6 (8) ◽  
pp. 205
Author(s):  
Binayini Bhagat ◽  
D. P. Satapathy
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Physico Chemical ◽  
Artificial Neural

Water is one of the prime elements responsible for subsistence on the earth. The scarcity of potable water is gradually increasing with the increase in population. The surface water quality is a very crucial and sensitive issue and is also a great environmental concern worldwide. Surface water pollution by physical, chemical, radiological and biological contaminants can be considered as an epidemic at times, all over the world. The present research work aims at assessing the water quality index (WQI) in the surface water of Brahmani river basin in Odisha by monitoring five sampling locations. The surface water samples data were subjected to comprehensive physico-chemical analysis besides general parameters. The monthly water quality parameters were collected and analyzed from five selected gauging stations of Odisha during the months of January to December from 2011 to 2016. Eleven physical, chemical and biological water quality parameters viz. pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Electrical Conductivity(EC), Nitrogen as nitrate (Nitrate-N), Total Coli-form Bacteria(TC), Fecal Coli-form Bacteria(FC), Chemical Oxygen Demand (COD), Nitrogen as ammonia (NH4-N), Total Alkalinity (TA) as CaCO3, Total Hardness (TH) as CaCO3 were selected for the analysis. Analysis of water quality for Brahmani River is done by Water Quality Index (WQI). Prediction of water quality index is done by using Artificial Neural Network (ANN).  It is apparent from WQI values that Talcher and Panposh recorded the water quality as moderate to poor and nearly unsuitable during the years 2011-2016 indicating water as not safe for domestic purposes and needs treatment, the WQI values of Kamalanga ranged from good to poor and the WQI values of Aul and Pottamundai ranged from good to moderate. Eleven physico-chemical parameters were involved in this analysis as input variables and water quality index as output variable. Two models were proposed to identify the most effective model in an attempt to predict the WQI.  Correlation between the parameters was carried out to find out the significant parameters affecting WQI. The ANN developed was trained and tested successfully using the available data sets and the performance of ANN models were determined by coefficient of determination (R2) and Root Mean Square Error (RMSE). Results show that ANN-1 gives the higher value of R2 in summer, monsoon and winter season (0.989, 0.976 and 0.959) and low RMSE (2.1865, 2.0768 and1.9657) as compared to that of the second model (ANN-2) which gives R2 value as 0.933, 0.945 and 0.943 and RMSE value as 2.8765, 2.5456 and 1.2745 for summer, monsoon and winter seasons respectively. Hence this study triggered the use of Artificial Neural Network to predict the Water Quality Index (WQI) rather than using the traditional WQI equation.

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