Assessing the viability of the non-monetary flood insurance market for Bangladeshi smallholder farmers

Hossain, Md. Shakhawat

doi:10.1007/s11069-024-06454-y

Cited by 3 publications

(1 citation statement)

References 75 publications

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“…Lack of early warnings left people with insufficient time to evacuate, exacerbating the impact (Kumar et al, 2022;Erdianto, 2023). This tragedy inspired the selection of a study area in northern Bangladesh to enhance flood prediction accuracy (Sarkar et al, 2022;Hossain, 2024).…”

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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Assessing smallholder farmers’ flood risk behavior and their willingness to pay for crop insurance as a risk coping strategy in northern Bangladesh

Hossain

2024

Nat Hazards

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Producer Welfare Benefits of Rating Area Yield Crop Insurance

Falsafian,

Ghahremanzadeh,

Aref Eshghi

et al. 2024

Agriculture

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Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran.

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Assessing the viability of the non-monetary flood insurance market for Bangladeshi smallholder farmers

Cited by 3 publications

References 75 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

Assessing smallholder farmers’ flood risk behavior and their willingness to pay for crop insurance as a risk coping strategy in northern Bangladesh

Producer Welfare Benefits of Rating Area Yield Crop Insurance

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