Inmagic DB/Text WebPublisher PRO: 3 records

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1 Sharma, R.; Poleman, T. T. 1993. The new economics of India’s green revolution: Income and employment diffusion in Uttar Pradesh. Ithaca, NY, USA: Cornell University Press. xix, 272p.

Green revolution ; Poverty ; Income distribution ; Households ; Villages ; Agrarian reform ; Irrigated farming ; Dairy farms ; Case studies / India / Uttar Pradesh / Walidpur / Rampur / Izarpur / Jamalpur / Meerut / Sitapur

(Location: IWMI-HQ Call no: 338.1 G635 SHA Record No: H038557)

2 Mia, Md. U.; Rahman, M.; Elbeltagi, A.; Abdullah-Al-Mahbub, Md.; Sharma, G.; Islam, H. M. T.; Pal, S. C.; Costache, R.; Towfiqul Islam, A. R. Md.; Islam, Md. M.; Chen, N.; Alam, E.; Washakh, R. M. A. 2022. Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology. Geocarto International, 30p. (Online first) [doi: https://doi.org/10.1080/10106049.2022.2112982]

Flooding ; Risk assessment ; Disaster risk management ; Machine learning ; Blockchain technology ; Neural networks ; Sustainable development ; Floodplains ; Rain ; Forecasting ; Datasets ; Mapping ; Normalized difference vegetation index ; Models / Bangladesh / Brahmaputra River / Jamalpur / Gaibandha / Kurigram / Bogra

(Location: IWMI HQ Call no: e-copy only Record No: H051339)

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2112982
https://vlibrary.iwmi.org/pdf/H051339.pdf

(5.41 MB) (5.41 MB)

The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.

3 Jerin, T.; Azad, M. A. K.; Khan, M. N. 2023. Climate change-triggered vulnerability assessment of the flood-prone communities in Bangladesh: a gender perspective. International Journal of Disaster Risk Reduction, 95:103851. (Online first) [doi: https://doi.org/10.1016/j.ijdrr.2023.103851]

Climate change ; Gender ; Women ; Men ; Flooding ; Communities ; Assessment ; Vulnerability ; Rainfall ; Livelihoods ; Decision making ; Food insecurity ; Rural areas ; Households ; Political aspects ; Risk reduction ; Socioeconomic aspects ; Indicators ; Drinking water ; Social networks / Bangladesh / Jamalpur

(Location: IWMI HQ Call no: e-copy only Record No: H052122)

https://vlibrary.iwmi.org/pdf/H052122.pdf

(5.75 MB)

Despite a significant development in vulnerability scholarship, how climatic drivers compounding with non-climatic forces cause differential vulnerability to climatic change is very scant. The purpose of this research is to illustrate the differential vulnerability of rural populations to floods in Bangladesh. To achieve this goal, empirical data – both primary and secondary – were procured. A quantitative research design was applied using a structured interview technique to collect field data. Secondary data on rainfall and temperature were collected from the Bangladesh Meteorological Department (BMD). We assessed gender differential vulnerability using Hahn et al.’s Livelihood Vulnerability Index (LVI). Our empirical findings revealed that women had a greater vulnerability to flooding with an LVI score of 0.550 compared to their men counterparts (0.484). Vulnerability varies in terms of health, water, food, sanitation, socio-demographic aptitudes, and agriculture-based livelihood, and floods. We also found that women's adaptive capacities (e.g., knowledge, and skills) were more potential to undermine flood vulnerability. While both men and women experienced high flood exposure, women were highly sensitive to flood hazards because of their social roles, locations, unequal access to decision-making power, and resource entitlements. Also, the intersection of diverse social disparities undermines adaptive capacity and reshapes exposure and sensitivity to floods. Our research, therefore, suggested that risk-driven plans and policy interventions are required to reduce the impacts of intersectional factors that cause greater gender-differentiated vulnerability. Future research further can examine how places and multifaced social factors interact and intersect in producing differential susceptibility to climate change in the developing world.