Statistical forecasting of tropical cyclone landfall activities over the North Indian Ocean rim countries

Wahiduzzaman, Md; Yeasmin, Alea

doi:10.1016/j.atmosres.2019.04.034

Cited by 27 publications

(7 citation statements)

References 73 publications

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“…It is widely used for the forecasts associated with different topics. Wahiduzzaman and Yeasmin [9] used PR for rainfall forecast over the North Indian Ocean rim countries. The outcome is an %31 improvement above climatology.…”

Section: Literature Reviewmentioning

confidence: 99%

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Yiğit

Esnaf

Yalçin

2021

Turkish Journal of Forecasting

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Demand forecasting is a complicated task due to incomplete data and unpredictability. Accurate demand forecasting has a direct impact on the performance of a company. The goal of the study is to present a new two-stage combination model named Hybrid-2-Best, for accurate demand forecasting. The model combines three forecasting models in a single combined forecast. The Hybrid-2-Best model uses a two-stage algorithm to achieve betterperforming forecasts. Case study showed that the proposed Hybrid-2-Best model performs the best forecast performance among other combination techniques and individual methods. Furthermore, GP integration in the first and second stages gives flexibility. Experimental results indicate that the proposed Hybrid-2-Best model is a promising alternative for sales demand forecasting. MAPE of the proposed model is 0,13. This is a good result and better than compared other models. Proposed model performed better than other compared models in MASE and MSE as well.

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Section: Literature Reviewmentioning

confidence: 99%

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Yiğit

Esnaf

Yalçin

2021

Turkish Journal of Forecasting

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“…Geographically weighted regression (GWR) is a technique that analyzes the variables in spatially and models their local relationships [29,30]. GWR identifies spatial heterogeneities in regression models [31][32][33][34] of geo-referenced data. The development of GWR includes a number of techniques like local regression and smoothing, spatial autocorrelation, kernel bandwidths, generalized linear modelling and Bayesian GWR [35].…”

Section: Spatial Modelling and Geographical Weighted Regression For Bmentioning

confidence: 99%

Spatial Modelling of Bacterial Diversity over the Selected Regions in Bangladesh by Next-Generation Sequencing: Role of Water Temperature

et al. 2020

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In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model's fitness. The spatial model has the potential to predict the bacterial diversity in the selected regions of Bangladesh.

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“…A generalized additive model (GAM) is a powerful and simple technique and easy to interpret based on the flexibility and regularization of prediction functions [32][33][34][35][36][37] Modeling studies will be a great societal value when the risk-mitigation policies are considered and future planning efforts are focused to the most high-risk areas [38][39][40][41][42]. In the present study, we have used a kernel density estimation approach and GAM to observe the typhoon characteristics in the WNP basin.…”

Section: Introductionmentioning

confidence: 99%

A Kernel Density Estimation Approach and Statistical Generalized Additive Model of Western North Pacific Typhoon Activities

2022

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This paper presents a development of a statistical model of typhoon genesis, tracks based on kernel density estimation and a generalized additive model (GAM). Modeling of typhoon activity is ultimately beneficial to the people living in coastal zones, insurance/re-insurance companies, policy, planning and decision departments. A 50-year record (1972–2021) of typhoon track observations from the International Best Track Archive Climate Stewardship have been used to observe the distribution of typhoon genesis by kernel density estimation. The tracks are simulated through the development of a GAM. It reproduces the observation well. A distance calculation approach between observed and simulated tracks’ landfall have been used to validate the model and the model shows a very good skill (approximately 75%).

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Statistical forecasting of tropical cyclone landfall activities over the North Indian Ocean rim countries

Cited by 27 publications

References 73 publications

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Spatial Modelling of Bacterial Diversity over the Selected Regions in Bangladesh by Next-Generation Sequencing: Role of Water Temperature

A Kernel Density Estimation Approach and Statistical Generalized Additive Model of Western North Pacific Typhoon Activities

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