Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization

Bagtzoglou, Amvrossios C.; Hossain, Faisal

doi:10.1007/s00477-008-0262-2

Cited by 19 publications

(4 citation statements)

References 87 publications

(72 reference statements)

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“…For instance: The PNN processing time is quicker than BPNN and Robust and noisy. The PNN manner of training is Simple and Immediate ( 15 , 28 – 31 ).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

Dhamodharavadhani

Rathipriya

Chatterjee³

2020

Front. Public Health

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The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and “R,” a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.

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“…For instance: The PNN processing time is quicker than BPNN and Robust and noisy. The PNN manner of training is Simple and Immediate ( 15 , 28 – 31 ).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

Dhamodharavadhani

Rathipriya

Chatterjee³

2020

Front. Public Health

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“…A method of estimating source location and time history in heterogeneous sites was proposed in [7] using particle methods. Bagtzoglou and Hossain [9] investigated different types of source information estimation problems using finite difference methods. By using this method, they approximated the groundwater flow equation and the transport equation in two dimensions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of groundwater contamination in an open landfill area using a novel hybrid clustering-based ai model

Singh,

Mahor,

Lakshmaiya

et al. 2024

Environ. Protect. Eng.

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Groundwater is a vital resource that provides drinking water to over half of the world’s population. However, groundwater contamination has become a serious issue due to human activities such as industrialization, agriculture, and improper waste disposal. The impacts of groundwater contamination can be severe, including health risks, environmental damage, and economic losses. A list of unknown groundwater contamination sources has been developed for the Wang-Tien landfill using a groundwater modeling system (GMS). Further, AI-based models have been developed which accurately predict the contamination from the sources at this site. A serious complication with most previous studies using artificial neural networks (ANN) for contamination source identification has been the large size of the neural networks. We have designed the ANN models which use three different ways of presenting inputs that are categorized by hierarchical K-means clustering. Such an implementation reduces the overall complexity of the model along with high accuracy. The predictive capability of developed models was assessed using performance indices and compared with the ANN models. The results show that the hybrid model of hierarchical K-means clustering and ANN model (HCA-ANN) is a highly accurate model for identifying pollution sources in contaminated water.

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“…Work in [9] investigated different types of source information estimation problems using nite difference methods. By using this method, they approximated the groundwater ow equation and the transport equation in two dimensions.…”

mentioning

confidence: 99%

Prediction of Groundwater Contamination in an Open Landfill Area Using a Novel Hybrid Clustering Based AI Model

Singh¹,

Mahor²

2023

Preprint

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Groundwater is a vital resource that provides drinking water to over half of the world's population. However, groundwater contamination has become a serious issue due to human activities such as industrialization, agriculture, and improper waste disposal. The impacts of groundwater contamination can be severe, including health risks, environmental damage, and economic losses. In this research work a list of unknown groundwater contamination sources were developed for the Wang-Tien landfill using Groundwater Modeling System (GMS). Further, AI based models have been developed which accurately predicts the contamination from the sources at this site. A serious complication with most previous studies using Artificial Neural Network (ANN) for contamination source identification has been the large size of the neural networks. To simplify this, we have designed the ANN models which use three different ways of presenting inputs that are categorized by hierarchical k-means clustering. Such an implementation reduces the overall complexity of the model along with high accuracy. The predictive capability of developed models was assessed using performance indices and compared with the ANN models. The results show that the Hybrid model of hierarchical K-means Clustering and ANN model (HCA-ANN) is highly accurate model for identifying pollution sources in contaminated water.

show abstract

Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization

Cited by 19 publications

References 87 publications

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

Prediction of groundwater contamination in an open landfill area using a novel hybrid clustering-based ai model

Prediction of Groundwater Contamination in an Open Landfill Area Using a Novel Hybrid Clustering Based AI Model

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