International Journal of Engineering Applied Sciences and Technology
The present work aims at correlating stream gauging stations along river Krishna of the state of ... more The present work aims at correlating stream gauging stations along river Krishna of the state of Maharashtra, India using Artificial Neural Networks. For this ANN models were developed with stream flow at the upstream stations(s) as inputs and stream flow at the downstream station as output. All the models show excellent results and prove the ability of ANNs to offer solutions with limited amount of data. The models will be useful to develop a decision support system for the downstream locations especially in case of flood events.
Flood is a natural phenomenon and generally causes heavy damage to natural systems as well as hum... more Flood is a natural phenomenon and generally causes heavy damage to natural systems as well as human lives. Hence to avoid adverse effects and losses caused by flood, 'flood Routing' is necessary.. Traditional physics based methods require exogenous data and are generally time consuming whereas numerical model like: MIKE 11, can work with the available data with less time period along with improved accuracy. Therefore researchers tend towards the numerical models rather than the traditional ones. Considering this, present work aims in reviewing different applications of MIKE 11: a numerical model for flood routing. Though this type of review is not an innovation in research but certainly can provide a useful trail to upcoming research. https://journalnx.com/journal-article/20150707
Abstract Concrete carbonation is considered an important problem in both the Civil Engineering an... more Abstract Concrete carbonation is considered an important problem in both the Civil Engineering and Materials Science fields. Over time, the properties of concrete change because of the interaction between the material and the environment and, consequently, its durability is affected. Conventionally, concrete carbonation depth at a given time under steady-state conditions can reasonably be estimated using Fick's second law of diffusion. This study addresses the statistical modelling of the concrete carbonation phenomenon, using a large number of results (827 specimens or samples, i.e. 827 is the number of data concerning the measurement of the carbonation coefficient in concrete test specimens), collected in the literature. Artificial Neural Networks (ANNs) and Genetic Programming (GP) were the Soft Computing techniques used to predict the carbonation coefficient, as a function of a set of conditioning factors. These models allow the estimation of the carbonation coefficient and, accordingly, carbonation as a function of the variables considered statistically significant in explaining this phenomenon. The results obtained through Artificial Neural Networks and Genetic Programming were compared with those obtained through Multiple Linear Regression (MLR) (which has been previously used to model the carbonation coefficient of concrete). The results reveal that ANNs and GP models present a better performance when compared with MLR, being able to deal with the nonlinear influence of relative humidity on concrete carbonation, which was the main limitation of MLR in modelling the carbonation coefficient in previous study. ANNs are commonly seen as a black box; in this study, an attempt is made to address this issue through Knowledge Extraction (KE) from trained weights and biases. KE helps to understand the influence of each input on the output and the influences identified by the KE technique are in accordance with general knowledge.
In the support of all ocean-related activities, it is necessary to predict the actual seawater le... more In the support of all ocean-related activities, it is necessary to predict the actual seawater levels as accurate as possible. The present work aims at forecasting the water levels from 3 to 6 weeks in advance at three locations: Dauphin Island, AL (Gulf of Mexico); Portland, ME (Gulf of Maine); and Cordova, AK (Gulf of Alaska) of divergent oceanic environment along the US coastline using neuro-wavelet technique (NWT) which is a combined approach of wavelet transform (WT) and artificial neural network. For this, time series of water-level anomaly (difference between the observed water level and harmonically predicted tidal level) was used to develop the NWT models at respective stations to predict the water levels for three different lead times from 3 to 6 weeks ahead. For this, hourly observed water levels along with harmonic tides were obtained from the National Oceanic and Atmospheric Administration of USA. The time series of water-level anomaly was decomposed using discrete wave...
As more than a quarter of India’s population resides along the coastlines, it is of utmost import... more As more than a quarter of India’s population resides along the coastlines, it is of utmost importance to predict the significant wave height as accurately as possible to cater the needs of safe and secure life. Presently Indian National Centre for Ocean Information Services (INCOIS) provides wave height forecasts on regional as well as local level ranging from 3 hours to 7 days ahead using numerical models. It is evident from numerical model forecasts at specific locations that the significant wave heights are not predicted very accurately. The obvious reason behind this is the ‘wind’ used in these models as a forcing function is itself forecasted wind (ECMWF wind (European Centre for Medium-range Weather Forecasting)) and hence many times the forecasts, differ very largely from the actual observations. These models work on larger grid size making it as major impediment in employing them particularly for location specific forecasts even though they work reasonably well for regional level. Present work aims in reducing the error in numerical wave forecast made by INCOIS at four stations along Indian coastline. For this ‘error’ between forecasted and observed wave height at current and previous time steps was used as input to predict the error 24 hr ahead in advance using ANN since it has been effectively used for wave forecasting (univariate time series forecasting in general) since last two decades or so. This predicted error was then added or subtracted from numerical wave forecast to improve the prediction accuracy. It is observed that numerical model forecast improved considerably when the predicted error was added or subtracted from it. This hybrid approach will add to the usefulness of the wave forecasts given by INCOIS to its stake holders. The performance of improved wave heights is judged by correlation coefficient and other error measures like RMSE, MAE and CE.
In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) b... more In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing ...
International Journal of Engineering Applied Sciences and Technology
The present work aims at correlating stream gauging stations along river Krishna of the state of ... more The present work aims at correlating stream gauging stations along river Krishna of the state of Maharashtra, India using Artificial Neural Networks. For this ANN models were developed with stream flow at the upstream stations(s) as inputs and stream flow at the downstream station as output. All the models show excellent results and prove the ability of ANNs to offer solutions with limited amount of data. The models will be useful to develop a decision support system for the downstream locations especially in case of flood events.
Flood is a natural phenomenon and generally causes heavy damage to natural systems as well as hum... more Flood is a natural phenomenon and generally causes heavy damage to natural systems as well as human lives. Hence to avoid adverse effects and losses caused by flood, 'flood Routing' is necessary.. Traditional physics based methods require exogenous data and are generally time consuming whereas numerical model like: MIKE 11, can work with the available data with less time period along with improved accuracy. Therefore researchers tend towards the numerical models rather than the traditional ones. Considering this, present work aims in reviewing different applications of MIKE 11: a numerical model for flood routing. Though this type of review is not an innovation in research but certainly can provide a useful trail to upcoming research. https://journalnx.com/journal-article/20150707
Abstract Concrete carbonation is considered an important problem in both the Civil Engineering an... more Abstract Concrete carbonation is considered an important problem in both the Civil Engineering and Materials Science fields. Over time, the properties of concrete change because of the interaction between the material and the environment and, consequently, its durability is affected. Conventionally, concrete carbonation depth at a given time under steady-state conditions can reasonably be estimated using Fick's second law of diffusion. This study addresses the statistical modelling of the concrete carbonation phenomenon, using a large number of results (827 specimens or samples, i.e. 827 is the number of data concerning the measurement of the carbonation coefficient in concrete test specimens), collected in the literature. Artificial Neural Networks (ANNs) and Genetic Programming (GP) were the Soft Computing techniques used to predict the carbonation coefficient, as a function of a set of conditioning factors. These models allow the estimation of the carbonation coefficient and, accordingly, carbonation as a function of the variables considered statistically significant in explaining this phenomenon. The results obtained through Artificial Neural Networks and Genetic Programming were compared with those obtained through Multiple Linear Regression (MLR) (which has been previously used to model the carbonation coefficient of concrete). The results reveal that ANNs and GP models present a better performance when compared with MLR, being able to deal with the nonlinear influence of relative humidity on concrete carbonation, which was the main limitation of MLR in modelling the carbonation coefficient in previous study. ANNs are commonly seen as a black box; in this study, an attempt is made to address this issue through Knowledge Extraction (KE) from trained weights and biases. KE helps to understand the influence of each input on the output and the influences identified by the KE technique are in accordance with general knowledge.
In the support of all ocean-related activities, it is necessary to predict the actual seawater le... more In the support of all ocean-related activities, it is necessary to predict the actual seawater levels as accurate as possible. The present work aims at forecasting the water levels from 3 to 6 weeks in advance at three locations: Dauphin Island, AL (Gulf of Mexico); Portland, ME (Gulf of Maine); and Cordova, AK (Gulf of Alaska) of divergent oceanic environment along the US coastline using neuro-wavelet technique (NWT) which is a combined approach of wavelet transform (WT) and artificial neural network. For this, time series of water-level anomaly (difference between the observed water level and harmonically predicted tidal level) was used to develop the NWT models at respective stations to predict the water levels for three different lead times from 3 to 6 weeks ahead. For this, hourly observed water levels along with harmonic tides were obtained from the National Oceanic and Atmospheric Administration of USA. The time series of water-level anomaly was decomposed using discrete wave...
As more than a quarter of India’s population resides along the coastlines, it is of utmost import... more As more than a quarter of India’s population resides along the coastlines, it is of utmost importance to predict the significant wave height as accurately as possible to cater the needs of safe and secure life. Presently Indian National Centre for Ocean Information Services (INCOIS) provides wave height forecasts on regional as well as local level ranging from 3 hours to 7 days ahead using numerical models. It is evident from numerical model forecasts at specific locations that the significant wave heights are not predicted very accurately. The obvious reason behind this is the ‘wind’ used in these models as a forcing function is itself forecasted wind (ECMWF wind (European Centre for Medium-range Weather Forecasting)) and hence many times the forecasts, differ very largely from the actual observations. These models work on larger grid size making it as major impediment in employing them particularly for location specific forecasts even though they work reasonably well for regional level. Present work aims in reducing the error in numerical wave forecast made by INCOIS at four stations along Indian coastline. For this ‘error’ between forecasted and observed wave height at current and previous time steps was used as input to predict the error 24 hr ahead in advance using ANN since it has been effectively used for wave forecasting (univariate time series forecasting in general) since last two decades or so. This predicted error was then added or subtracted from numerical wave forecast to improve the prediction accuracy. It is observed that numerical model forecast improved considerably when the predicted error was added or subtracted from it. This hybrid approach will add to the usefulness of the wave forecasts given by INCOIS to its stake holders. The performance of improved wave heights is judged by correlation coefficient and other error measures like RMSE, MAE and CE.
In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) b... more In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing ...
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