scholarly journals River Water Level Prediction Modelling using Artificial Neural Network and Multiple Linear Regression

2019 ◽  
Vol 09 (06) ◽  
pp. 23-31
Author(s):  
Pankaj Goswami ◽  
Haina Brahma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Level ◽  
River Water ◽  
Artificial Neural ◽  
Prediction Modelling ◽  
Water Level Prediction

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

2020 ◽  
Vol 303 ◽  
pp. 122926 ◽  
Author(s):  
Ahmad Hosseinzadeh ◽  
Mansour Baziar ◽  
Hossein Alidadi ◽  
John L. Zhou ◽  
Ali Altaee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Nutrient Recovery ◽  
Artificial Neural

Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques

2019 ◽  
Vol 20 (3) ◽  
pp. 800-808
Author(s):  
G. T. Patle ◽  
M. Chettri ◽  
D. Jhajharia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Relative Humidity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Accurate Estimation ◽  
Pan Evaporation ◽  
Ann Models ◽  
Artificial Neural

Abstract Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.

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