AI SearchApplication of artificial neural networks for rainfall forecasting in Mumbai
Badgujar, Girish
Application of artificial neural networks for rainfall forecasting in Mumbai - Pathum Thani, Thailand : Asian Institute of Technology, 2009 - 66 leaves : ill. - Thesis ; no. WM-08-05 . - Asian Institute of Technology. Thesis ; no. WM-08-05 .
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management
Thesis (M.Eng.) - Asian Institute of Technology, 2009
Accurate forecast of both spatial and temporal distribution of rainfall is necessary for flood management in urban areas. Mumbai is one of the metropolitan cities in India. It receives high intensity rainfall during four monsoon months. High intensity rainfall and poor drainage system causes flooding in Mumbai almost every year. In the present study three different short term rainfall forecasting models were developed, using ANN, to forecast rainfall from 1 to 4-hr ahead at Santa Cruz station in Mumbai. The inputs for the first model, Model A, were rainfall time series at Santa Cruz station, The inputs for Model B were rainfall and other meteorological namely, temperature, atmospheric pressure, relative humidity and wind speed of Santa Cruz station. The inputs for Model C were selected using Mutual Information technique from rainfall and meteorological variables of Santa Cruz station and surrounding Colaba station. Continuous time series data from June to September was used for model development. 2 and 1 year data was used for training and cross validation while 1 year data used for testing. Normalized Mean Square Error (NMSE) was used to evaluate the performance of the developed models. For 1-h forecast, the least NMSE (0.65) was observed in Model C. For 2, 3 and 4-h forecasts model B gives lower NMSE. Model A always produced high NMSE. It was found that MI technique successfully identified input variables for 1-h forecasts. Moreover, the rainfall at Santa Cruz station cannot be forecasted by only using historical time series of rainfall data. Higher lead period forecasts were found to be dependent on target station input variables.
Rain and rainfall--Forecasting--India--Mumbai
Application of artificial neural networks for rainfall forecasting in Mumbai - Pathum Thani, Thailand : Asian Institute of Technology, 2009 - 66 leaves : ill. - Thesis ; no. WM-08-05 . - Asian Institute of Technology. Thesis ; no. WM-08-05 .
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management
Thesis (M.Eng.) - Asian Institute of Technology, 2009
Accurate forecast of both spatial and temporal distribution of rainfall is necessary for flood management in urban areas. Mumbai is one of the metropolitan cities in India. It receives high intensity rainfall during four monsoon months. High intensity rainfall and poor drainage system causes flooding in Mumbai almost every year. In the present study three different short term rainfall forecasting models were developed, using ANN, to forecast rainfall from 1 to 4-hr ahead at Santa Cruz station in Mumbai. The inputs for the first model, Model A, were rainfall time series at Santa Cruz station, The inputs for Model B were rainfall and other meteorological namely, temperature, atmospheric pressure, relative humidity and wind speed of Santa Cruz station. The inputs for Model C were selected using Mutual Information technique from rainfall and meteorological variables of Santa Cruz station and surrounding Colaba station. Continuous time series data from June to September was used for model development. 2 and 1 year data was used for training and cross validation while 1 year data used for testing. Normalized Mean Square Error (NMSE) was used to evaluate the performance of the developed models. For 1-h forecast, the least NMSE (0.65) was observed in Model C. For 2, 3 and 4-h forecasts model B gives lower NMSE. Model A always produced high NMSE. It was found that MI technique successfully identified input variables for 1-h forecasts. Moreover, the rainfall at Santa Cruz station cannot be forecasted by only using historical time series of rainfall data. Higher lead period forecasts were found to be dependent on target station input variables.
Rain and rainfall--Forecasting--India--Mumbai