Your search found 4 records
1 Le Moigne, G.; Barghouti, S.; Feder, G.; Garbus, L.; Xie, M.. (Eds.) 1992. Country experiences with water resources management: Economic, institutional, technological and environmental issues. Washington, DC, USA: World Bank. 213p. (World Bank technical paper no.175)
Water resources ; Water supply ; Case studies ; Research ; Environmental effects ; Irrigation / East Asia / Africa
(Location: IWMI-HQ Call no: 333.91 G000 LEM Record No: H010850)
2 Xie, M.. 1996. Water resources in Vietnam. In World Bank; ADB; FAO; UNDP; MARD; NGO Water Resources Group; Vietnam. Institute of Water Resources Planning. Vietnam: Water resources sector review - Selected working papers. A selection of unpublished working papers prepared for the "Sector review" and for the Workshop held in Hanoi in July 1995. 43p.
Water resources ; Surface water ; Assessment ; Infrastructure ; Flood control ; Drainage ; Groundwater ; River basins ; Water shortage ; Water quality ; Salinity ; Sedimentation ; Irrigated farming ; Rice ; Energy ; Hydrology ; Monitoring / Vietnam
(Location: IWMI-HQ Call no: 333.91 G784 WOR Record No: H022608)
3 Xie, M.; Kuffner, U.; Le Moigne, G. 1993. Using water efficiently: Technological options. Washington, DC, USA: World Bank. 52p. (World Bank Technical Paper No.205)
Water use efficiency ; Irrigation management ; Water supply ; Seepage ; Percolation ; Evapotranspiration ; Sprinkler irrigation ; Drip irrigation ; Water reuse ; River basin management ; Policy
(Location: IWMI HQ Call no: 631.7 G000 XIE Record No: H040282)
4 Jean, N.; Burke, M.; Xie, M.; Davis, W. M.; Lobell, D. B.; Ermon, S. 2016. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301):790-794. [doi: https://doi.org/10.1126/science.aaf7894]
Poverty ; Satellite imagery ; Forecasting ; Living standards ; Household consumption ; Household expenditure ; Machine learning ; Neural networks ; Models ; Performance evaluation ; Economic aspects ; Assets / Nigeria / Tanzania / Uganda / Malawi / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H047755)
(7.12 MB)
Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.
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