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Integrated system for early childhood development
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Empowering women and children through the usage of ict[1]
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Sas en – a swarm of agents for a sustainble environnent
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Iseced presentation at young enterprinue challenge organized by british vouncil and slassc om
Iseced presentation at young enterprinue challenge organized by british vounc...
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The steps are as follows: Step 1: installing required libraries to R environment Step 2: Generating Token Key from Facebook Step 3: Retrieving Information from Facebook Step 4: Advance Graphical Representation
Statistical analysis of facebook using r
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Pixel Clear (Pvt) Ltd
Even though standard precipitation index (SPI) is not a drought predicting tool using downscaled results it was possible to forecast SPI. Forecasted SPI can use for two purposes. Firstly, in order to implement better drought relief payment policy. Secondly, to better water resource planning in order to reduce agriculture risk. Statistical downscaling procedure was developed over Sri Lanka for drought risk. The system was based on Climate Information tool Kit 2.0 (CLIK) by Asia-Pacific Economic Corporation (APEC) Climate Center (APCC). As the predictor region El Niño Southern Oscillation (ENSO) region was considered because recent droughts are more influenced by ENSO compared to traditional South West monsoons (SWM) and North East monsoon (NEM) for the island. Seasonal drought study was carried out considering 3 months intervals for 4 seasons January to March (JFM), April to June (AMJ), July to September (JAS) and October to November (OND). In order to identify optimal SPI scales, models, variables and NINO regions for each season a preliminary study was carried out using a sample stations and based on the results detailed study was carried out for 34 stations covering central and southern parts of the island. Heidke skill score was considered as the categorical verification measure. In order to improve the magnitude of forecasted SPI 2 variance inflation techniques were employed. In CLIK multi model ensemble (MME) predictions were tested considering 3 deterministic models: simple composition method (SCM), multiple regression method (MRG) and synthetic multi model super ensemble method (SSE). However due to low resolution regression based downscaling was considered. Interestingly central high hills showed a very high correlation with ENSO during OND. For highly skilled stations drought characteristics such as trends, onset, duration, frequency and severity can be calculate. Our results highlight that CLIK is skillful over Sri Lanka. Specially in identifying best downscaling characteristics over a station.
Downscaling global climate model outputs to fine scales sanjaya ratnayake
Downscaling global climate model outputs to fine scales sanjaya ratnayake
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Integrated system for early childhood development
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Empowering women and children through the usage of ict[1]
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Sas en – a swarm of agents for a sustainble environnent
Sas en – a swarm of agents for a sustainble environnent
Sas en – a swarm of agents for a sustainble environnent
Pixel Clear (Pvt) Ltd
Iseced presentation at young enterprinue challenge organized by british vouncil and slassc om
Iseced presentation at young enterprinue challenge organized by british vounc...
Iseced presentation at young enterprinue challenge organized by british vounc...
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The steps are as follows: Step 1: installing required libraries to R environment Step 2: Generating Token Key from Facebook Step 3: Retrieving Information from Facebook Step 4: Advance Graphical Representation
Statistical analysis of facebook using r
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Pixel Clear (Pvt) Ltd
Even though standard precipitation index (SPI) is not a drought predicting tool using downscaled results it was possible to forecast SPI. Forecasted SPI can use for two purposes. Firstly, in order to implement better drought relief payment policy. Secondly, to better water resource planning in order to reduce agriculture risk. Statistical downscaling procedure was developed over Sri Lanka for drought risk. The system was based on Climate Information tool Kit 2.0 (CLIK) by Asia-Pacific Economic Corporation (APEC) Climate Center (APCC). As the predictor region El Niño Southern Oscillation (ENSO) region was considered because recent droughts are more influenced by ENSO compared to traditional South West monsoons (SWM) and North East monsoon (NEM) for the island. Seasonal drought study was carried out considering 3 months intervals for 4 seasons January to March (JFM), April to June (AMJ), July to September (JAS) and October to November (OND). In order to identify optimal SPI scales, models, variables and NINO regions for each season a preliminary study was carried out using a sample stations and based on the results detailed study was carried out for 34 stations covering central and southern parts of the island. Heidke skill score was considered as the categorical verification measure. In order to improve the magnitude of forecasted SPI 2 variance inflation techniques were employed. In CLIK multi model ensemble (MME) predictions were tested considering 3 deterministic models: simple composition method (SCM), multiple regression method (MRG) and synthetic multi model super ensemble method (SSE). However due to low resolution regression based downscaling was considered. Interestingly central high hills showed a very high correlation with ENSO during OND. For highly skilled stations drought characteristics such as trends, onset, duration, frequency and severity can be calculate. Our results highlight that CLIK is skillful over Sri Lanka. Specially in identifying best downscaling characteristics over a station.
Downscaling global climate model outputs to fine scales sanjaya ratnayake
Downscaling global climate model outputs to fine scales sanjaya ratnayake
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Practices of Downscaling Methods for Water Resources Management in Sri Lanka
Practices of Downscaling Methods for Water Resources Management in Sri Lanka
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