Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques

Sahoo, Bhabagrahi; Bhaskaran, Prasad K.

doi:10.1007/s00500-019-03775-0

Cited by 21 publications

(4 citation statements)

References 42 publications

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“…In a subsequent work [42], they successfully applied soft-computing techniques to predict the maximum inundation extent for about 200 coastal locations given wind speed, angle of approach, landfall locations and translation speed as inputs. In another study, Xu et al [43] used a hydrological-hydraulic model to generate synthetic data for the Light Gradient Boosting Machine (LightGBM) model to rapidly predict the maximum flood depth at 10 locations, showing comparable performance to physical models.…”

Section: Peak-value Forecastingmentioning

confidence: 99%

“…Hashemi et al [20] developed an efficient and robust Artificial Neural Network (ANN) model that outputted a vector, including peak storm surges at the Newport and Province stations, using the tropical storm parameters: central pressure, radius to maximum winds, forward velocity, and storm track. Sahoo and Bhaskaran [42] constructed surrogate models to predict the maximum storm tide at 200 locations along the Odisha coast using five parameters as inputs, including wind speed, approach angle, latitude, longitude and translation speed at the time of landfall. Nevertheless, it's noteworthy that there is no study comparing the performance of the two methods: building a single model to output multiple peak values at a time and developing multiple models derived from one model using transfer learning.…”

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confidence: 99%

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A Review of Application of Machine Learning in Storm Surge Problems

Qin,

Su,

Chu

et al. 2023

JMSE

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The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive accuracy through the application of ML. The readers are guided through the steps required to implement ML algorithms, from the first step of formulating problems to data collection and determination of input features to model selection, development and evaluation. Additionally, the review explores the application of hybrid methods, which combine the bilateral advantages of data-driven methods and physics-based models. Furthermore, the strengths and limitations of ML methods in predicting storm surges are thoroughly discussed, and research gaps are identified. Finally, we outline a vision toward a trustworthy and reliable storm surge forecasting system by introducing novel physics-informed ML techniques. We are meant to provide a primer for beginners and experts in coastal ocean sciences who share a keen interest in ML methodologies in the context of storm surge problems.

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Section: Peak-value Forecastingmentioning

confidence: 99%

mentioning

confidence: 99%

A Review of Application of Machine Learning in Storm Surge Problems

Qin,

Su,

Chu

et al. 2023

JMSE

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“…Việc sử dụng lập trình di truyền (GP) để dự báo nước biển dâng sau bão gần đây cũng đã được một số nghiên cứu áp dụng. Các tác giả trong bài báo [9] đã đề xuất sử dụng GP để dự đoán nước dâng do bão và ngập lụt do các cơn bão nhiệt đới. Các thí nghiệm được thực hiện trên các bộ dữ liệu từ bờ biển Odisha đến tiếp giáp với Vịnh Bengal.…”

Section: Lập Trình DI Truyền Cho Bài Toán Dự Báo Nước Biển Dâng Do Bãounclassified

Phương pháp dự báo nước biển dâng do bão dựa trên lập trình di truyền

Hiền¹,

Phúc²,

Mạnh³

et al. 2020

VNJHM

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“…Research directed to understanding tropical cyclones and their impacts is wide ranging. Many studies have been devoted to gaining a clear understanding of the processes of surge intensification and wind movement associated with tropical cyclones using spatial modeling and simulation techniques (Murty et al 2016;Singh et al 2019;Sahoo and Bhaskaran 2019a;2019b). Other studies have variously focused on risk and vulnerability assessment related to tropical cyclones using geospatial techniques (Quader et al 2017;Hoque et al 2018Hoque et al , 2019Mullick et al 2019;Alam et al 2020), cyclone resilience (Islam et al 2020), relationships between meteorological parameters and cyclone genesis (Kundu et al 2001), and physical changes on land and in oceans during cyclones (Chauhan et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal assessment of social resilience to tropical cyclones in coastal Bangladesh

Islam

Paull

Griffin

et al. 2021

Geomatics, Natural Hazards and Risk

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Tropical cyclones wreak havoc on the lives of coastal populations, especially in marginalised communities in developing countries where cyclone impacts are disproportionately lethal compared to developed countries. These populations must implement measures that enable them to prepare for, respond to, and recover from cyclone impacts. Using empirical evidence, this study presents a spatio-temporally explicit, index-based assessment technique to assess social resilience to tropical cyclones. The key components of social resilience were extracted by Principal Component Analysis; distinct resilience indices were generated and mapped for the three disaster phases. The most influential components were then aggregated into an Effective Social Resilience Index (ESoRI), which was mapped across the study area. The technique was applied in a coastal sub-district of Bangladesh to demonstrate the approach. Basic preparedness training, emergency response, social bonding, and reconstruction and rehabilitation strongly influenced resilience in ways that corresponded closely to the phases of the disaster-management cycle: preparedness, response, and recovery. The spatial pattern of resilience revealed that social resilience varies significantly in time and space within a circumscribed locale. Importantly, areas close to the sea and rivers were found to be more resilient despite their high exposure to cyclones and lack of external support. This study will guide policymakers concerned with resilience building on a priority basis such as those working in resource-scarce conditions of developing countries.

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Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques

Cited by 21 publications

References 42 publications

A Review of Application of Machine Learning in Storm Surge Problems

A Review of Application of Machine Learning in Storm Surge Problems

Phương pháp dự báo nước biển dâng do bão dựa trên lập trình di truyền

Spatio-temporal assessment of social resilience to tropical cyclones in coastal Bangladesh

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