A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improving Flood Prediction with Deep Learning Methods
https://orcid.org/http://orcid.org/0000-0003-4450-5599
Flood is a natural calamity and is needed to be controlled in a specific manner. In this paper, flood forecasting is carried out using Deep Belief Network (DBN) for the banks of river Daya and Bhargavi that flows across Odisha, India. A comparative study is done using other machine learning techniques to elaborately show the impact of barrage construction. The performance of DBN is established by comparing it with Teaching Learning-Based Optimization method (TLBO) with the use of many parameters like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The flood forecasting is done for 1 day, 1 week and 2 weeks of time period using both the rivers, i.e. Daya and Bhargavi with the use of DBN and TLBO technique.
Improving Flood Prediction with Deep Learning Methods
https://orcid.org/http://orcid.org/0000-0003-4450-5599
Flood is a natural calamity and is needed to be controlled in a specific manner. In this paper, flood forecasting is carried out using Deep Belief Network (DBN) for the banks of river Daya and Bhargavi that flows across Odisha, India. A comparative study is done using other machine learning techniques to elaborately show the impact of barrage construction. The performance of DBN is established by comparing it with Teaching Learning-Based Optimization method (TLBO) with the use of many parameters like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The flood forecasting is done for 1 day, 1 week and 2 weeks of time period using both the rivers, i.e. Daya and Bhargavi with the use of DBN and TLBO technique.
Improving Flood Prediction with Deep Learning Methods
https://orcid.org/http://orcid.org/0000-0003-4450-5599
Flood is a natural calamity and is needed to be controlled in a specific manner. In this paper, flood forecasting is carried out using Deep Belief Network (DBN) for the banks of river Daya and Bhargavi that flows across Odisha, India. A comparative study is done using other machine learning techniques to elaborately show the impact of barrage construction. The performance of DBN is established by comparing it with Teaching Learning-Based Optimization method (TLBO) with the use of many parameters like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The flood forecasting is done for 1 day, 1 week and 2 weeks of time period using both the rivers, i.e. Daya and Bhargavi with the use of DBN and TLBO technique.
Improving Flood Prediction with Deep Learning Methods
J. Inst. Eng. India Ser. B
Nayak, Monalisa (author) / Das, Soumya (author) / Senapati, Manas Ranjan (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1189-1205
2022-08-01
17 pages
Article (Journal)
Electronic Resource
English