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Perceiving the Trend of Terrestrial Climate Change during the Past 40 year (1978-2018) DOI:  https://doi.org/10.30564/jasr.v4i1.2488 Abstract In past few decades, climate has manifested numerous shifts in its trend. Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale. To understand the long term climate fluctuations, we have analyzed forty years (1978 - 2018) data of ten climatic parameters that are responsible to influence the climate dynamics. The parameters involved in the present study are total solar irradiance (TSI), ultra violet (UV) index, cloud cover, carbon dioxide (CO2) abundances, multivariate (ENSO) index, volcanic explosivity index (VEI), global surface temperature (GST) anomaly, global sea ice extent, global mean sea level and global precipitation anomaly. Using the above mentioned climate entities; we have constructed a proxy index to study the quantitative measure of the climate change. In thi...

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble DOI:  https://doi.org/10.30564/jcsr.v5i1.5303 Abstract Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant no...

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble DOI:  https://doi.org/10.30564/jcsr.v5i1.5303 Abstract Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant no...

Perceiving the Trend of Terrestrial Climate Change during the Past 40 year (1978-2018) DOI:  https://doi.org/10.30564/jasr.v4i1.2488 Abstract In past few decades, climate has manifested numerous shifts in its trend. Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale. To understand the long term climate fluctuations, we have analyzed forty years (1978 - 2018) data of ten climatic parameters that are responsible to influence the climate dynamics. The parameters involved in the present study are total solar irradiance (TSI), ultra violet (UV) index, cloud cover, carbon dioxide (CO2) abundances, multivariate (ENSO) index, volcanic explosivity index (VEI), global surface temperature (GST) anomaly, global sea ice extent, global mean sea level and global precipitation anomaly. Using the above mentioned climate entities; we have constructed a proxy index to study the quantitative measure of the climate change. In thi...

Perceiving the Trend of Terrestrial Climate Change during the Past 40 year (1978-2018) Abstract In past few decades, climate has manifested numerous shifts in its trend. Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale. To understand the long term climate fluctuations, we have analyzed forty years (1978 - 2018) data of ten climatic parameters that are responsible to influence the climate dynamics. The parameters involved in the present study are total solar irradiance (TSI), ultra violet (UV) index, cloud cover, carbon dioxide (CO2) abundances, multivariate (ENSO) index, volcanic explosivity index (VEI), global surface temperature (GST) anomaly, global sea ice extent, global mean sea level and global precipitation anomaly. Using the above mentioned climate entities; we have constructed a proxy index to study the quantitative measure of the climate change. In this process these indicators were aggregated to a si...