| 1. |
- Bhardwaj, Anshuman, et al.
(författare)
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Developing a statistical dengue risk prediction model for the state of delhi based on various environmental variables
- 2012
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Ingår i: International Journal of Geoinformatics. - 1686-6576. ; 8:3, s. 45-52
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Tidskriftsartikel (refereegranskat)abstract
- This work investigates dengue affected localities of Delhi using static and dynamic environmental factors and their possible spatial relationships. The static variables include soil drainage, built-up area and vegetation. The dynamic variables represent seasonal precipitation and temperature data for past hundred years. Significance test (t-test) provided deterministic evidence of variable importance to model. Weighted sum and quantile classification helped to create a final risk map. The model indicated non-uniform distribution of risk across the state and showed elevated risk in urban built-up areas mainly alongside the river Yamuna. Three years (2007, 2008 and 2009) data for confirmed dengue cases for affected localities were obtained from Municipal Corporation of Delhi (MCD) for validation. 57.98% of the reported cases were observed under high risk category as modeled in this study. Modeling results indicate that environmental factors like Precipitation, temperature, soil drainage, built-up area and vegetation govern mosquito breeding and are correlated with human dengue risk The approach verified that dengue risk can be modeled at the state level and can be modified for risk predictions of other vector-borne diseases in varied ecological regions
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| 2. |
- Sam, Lydia, et al.
(författare)
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Use of Geospatial Tools to Prioritize Zones of Hydro-Energy Potential in Glaciated Himalayan Terrain
- 2016
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Ingår i: Journal of the Indian Society of Remote Sensing. - : Springer Science and Business Media LLC. - 0255-660X .- 0974-3006. ; 44:3, s. 409-420
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Tidskriftsartikel (refereegranskat)abstract
- Sustainable development of the Himalayan region is directly linked to optimal utilization of available renewable resources. There is a need to first select the zones suitable for hydropower sites, and then to focus on them only; as purely field-based surveying of rugged mountainous regions for hydropower generation requires too much of time and effort. We used geospatial tools to identify suitable sites for hydropower generation. A Geographic Information System (GIS)-based tool called Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) was used for computing annual runoff volume using watershed-wise topography and biophysical variables. The zones suitable for hydropower generation were then identified based on calculated hydropower energy using derived runoff volumes and hydraulic head. The model accuracy was checked using well established efficiency criteria: coefficient of determination (R2 = 0.98), RMSE-observations standard deviation ratio (RSR), Percent bias (PBIAS) and Nash–Sutcliffe efficiency (NSE). For all these parameters, the model was found to be performing satisfactorily.
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