Application of Deep Neural Network for Predicting River Tide Level

Rasel, Risul Islam; Uddin, Md. Nizam; Islam, Fokhrul; Haroon, Amran

doi:10.1109/iciset.2018.8745593

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…They applied the SVM-BP model for flood forecasting in the Changhua River, showing that this combination outperformed other methods. (Rasel et al, 2018) forecasted tidal levels in the Karnafuli River, crucial for the daily activities of Chittagong residents such as fishing, waterway traffic, and port activity regulation. They developed a machine learning model using ten years of historical Karnafuli River datasets (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) and compared SVM, BP-ANN, and DNN algorithms, with DNN achieving 99% accuracy in water level prediction.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

Hasan,

Islam,

Fahmida

2024

ceddi j. inf. syst. technol.

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Floods are among the most devastating natural disasters, causing extensive damage to property and posing a threat to human lives. However, significant progress has been made in mitigating their impact through the development of effective early warning systems. Over the past two decades, advances in machine learning (ML) technology have played a crucial role in enhancing the predictive capabilities of these systems. A recent study focused on predicting floods in non-tidal rivers by proposing various machine-learning models. The research findings indicate that the Random Forest algorithm emerges as the most effective, offering an accuracy of 87% with high precision, recall, and F1 scores, using an 80:20 training and testing data ratio. These findings provide valuable insights for hydrologists and make a significant contribution to flood forecasting and mitigation efforts. The study has significant implications for flood understanding and management, offering a better understanding of machine learning model performance in predicting floods in non-tidal rivers. This research provides a solid foundation for the development of more efficient early warning systems. The information gleaned from this study can be utilized by hydrologists, climate scientists, and other related practitioners to develop more accurate and reliable forecasting strategies in the face of flood threats. Thus, this research is not only a valuable scientific contribution but also a practical tool for future flood disaster risk mitigation efforts.

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Section: Introductionmentioning

confidence: 99%

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

Hasan,

Islam,

Fahmida

2024

ceddi j. inf. syst. technol.

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A Review on Deep Learning Approaches to Forecasting the Changes of Sea Level

Luaran

Alfred

Obit

et al. 2021

Lecture Notes in Electrical Engineering

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The amalgamation of atmospheric elements indicates positive trends in sea level rise which has had a significant impact on nearly 60% of the world's population living in the low elevated coastal area. In this paper, we first discuss potential factors leading to the rise in sea level and negative impacts on future development along the coastal region.Then, methods of acquiring sea level data which revolutionize the study of variation at sea level will also be reviewed and discussed. The present paper aims to review several Deep Learning (DL) algorithms that address critical issues of forecasting, specifically a time variable known as time series by managing complex patterns and inefficiently capturing long-term multivariate data dependency. Asynchronous data handling required correct theoretical framework processes. Based on the review conducted, the deep learning architecture is capable of generating accurate prediction at sea level which can be used as decision-making tools for managing low-lying coastal areas.

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Application of Deep Neural Network for Predicting River Tide Level

Cited by 2 publications

References 18 publications

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

A Review on Deep Learning Approaches to Forecasting the Changes of Sea Level

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