Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data - a machine learning approach

Eltazarov, Sarvarbek; Bobojonov, Ihtiyor; Kuhn, Lena; Glauben, Thomas

doi:10.1080/20964471.2023.2196830

Cited by 4 publications

(2 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the relevant literature, we summarize three factors that influence the level of risk in the region: hazard factors, exposure factors and risk resistance factors, and use them as primary indicators in the evaluation system [4][5][6]. After further reflection and generalization, we concretized the indicators and refined eight specific indicators as the secondary indicators of the evaluation system.…”

Section: Establishment Of Evaluation Indicatorsmentioning

confidence: 99%

Research on extreme weather insurance and real estate decision model based on AHP-EWM-CVM and K-Means clustering technology

Han,

Lin

2024

HSET

View full text Add to dashboard Cite

Aiming at the challenge of extreme weather to insurance industries and real estate industries, this paper constructs an insurance model and a real estate decision model. The insurance model uses analytic hierarchy Process (AHP), entropy weight method (EWM), coefficient of variation method (CVM) and K-Means clustering algorithm to evaluate the risk level in different regions and predict the values of risk indicators up to the year 2035, and then calculate future insurance premiums. At the same time, multiple constraints are used to determine the future risk safety interval, which provides the basis for insurance companies to make decisions. In the case application, this paper finds that China is currently medium risk region and will be a medium risk region in the future, and obtains the time point when insurance companies can underwrite policies in this region. In addition, this paper also constructs a real estate decision model, which comprehensively considers three factors: risk, development potential and risk cost, and provides decision making basis for real estate developers through GE matrix. In the case application, this paper predicts that in the next nearly ten years, China is a medium willingness and medium risk area, suggesting that real estate developers should be cautious in deciding whether to develop. This study provides decision support for insurance industries and real estate industries to deal with extreme weather, which is of theoretical and practical significance.

show abstract

Section: Establishment Of Evaluation Indicatorsmentioning

confidence: 99%

Research on extreme weather insurance and real estate decision model based on AHP-EWM-CVM and K-Means clustering technology

Han,

Lin

2024

HSET

View full text Add to dashboard Cite

show abstract

“…Trinh et al [22] used Logistic regression, SVM, and RF to generate landslide susceptibility mapping in the Ha Giang province of Vietnam. Eltazarov et al [23] used RF to downscale gridded precipitation data and then investigate the risk reduction potential of weather index insurance. Zhang et al [24] developed a data-driven wind turbine fault detection technique with the help of XGBoost.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning

Wu,

Yang,

Zhang

et al. 2024

Atmosphere

View full text Add to dashboard Cite

As a developing country with an agricultural economy as a pillar, Bangladesh is highly vulnerable to adverse effects of climate change, so the generation of high-resolution temperature maps is of great value for Bangladesh to achieve agricultural sustainable development. However, Bangladesh’s weak economy and sparse meteorological stations make it difficult to obtain such maps. In this study, by mining internal features and links inside observed data, we developed an efficient data-driven downscaling technique to generate high spatial-resolution temperature distribution maps of Bangladesh directly from observed temperature data at 34 meteorological stations with irregular distribution. Based on these high-resolution historical temperature maps, we further explored a data-driven forecast technique to generate high-resolution temperature maps of Bangladesh for the period 2025–2035. Since the proposed techniques are very low-cost and fully mine internal links inside irregular-distributed observations, they can support relevant departments of Bangladesh to formulate policies to mitigate and adapt to climate change in a timely manner.

show abstract

Index-based insurance to mitigate current and future extreme events financial losses for water utilities

Gesualdo,

Benso,

Sass

et al. 2024

International Journal of Disaster Risk Reduction

View full text Add to dashboard Cite

Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data - a machine learning approach

Cited by 4 publications

References 82 publications

Research on extreme weather insurance and real estate decision model based on AHP-EWM-CVM and K-Means clustering technology

Research on extreme weather insurance and real estate decision model based on AHP-EWM-CVM and K-Means clustering technology

High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning

Index-based insurance to mitigate current and future extreme events financial losses for water utilities

Contact Info

Product

Resources

About