A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (2017)
This paper is about the discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (ISH Journal of Hydraulic Engineering, 29 December 2017, DOI: 10.1080/09715010.2017.1420497). The authors of the original paper presented the application of Back Propagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models to predict the water table depth of an aquifer by considering the precipitation, temperature, humidity, surface runoff and evapotranspiration loss as the input of the models. Five different input combinations were examined in the modeling. The authors concluded that the performance of ANFIS is better as compared to BPNN by considering several evaluation criteria. The discusser appreciates this type of study, and the results could be extremely useful for engineers or other audiences. Although the methodology and findings observed in the study are reasonable, this discussion calls attention to add a few issues that may require further clarification.
Discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (2017)
This paper is about the discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (ISH Journal of Hydraulic Engineering, 29 December 2017, DOI: 10.1080/09715010.2017.1420497). The authors of the original paper presented the application of Back Propagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models to predict the water table depth of an aquifer by considering the precipitation, temperature, humidity, surface runoff and evapotranspiration loss as the input of the models. Five different input combinations were examined in the modeling. The authors concluded that the performance of ANFIS is better as compared to BPNN by considering several evaluation criteria. The discusser appreciates this type of study, and the results could be extremely useful for engineers or other audiences. Although the methodology and findings observed in the study are reasonable, this discussion calls attention to add a few issues that may require further clarification.
Discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (2017)
Barati, Reza (author)
ISH Journal of Hydraulic Engineering ; 26 ; 468-471
2020-10-01
4 pages
Article (Journal)
Electronic Resource
Unknown
Modeling water table depth using adaptive Neuro-Fuzzy Inference System
| Taylor & Francis Verlag | 2019
| British Library Online Contents | 2019
Tribute to Subrata Kumar Chakrabarti
| Online Contents | 2009
Tribute to Subrata Kumar Chakrabarti
| Elsevier | 2009
Tribute to Subrata Kumar Chakrabarti
| Online Contents | 2009