03728nas a2200409 a 4500 20260817162834.0 110314s2009 th uu|m rtt 0| a1eng d .b12092265 AIT Thesis no.WM-08-05 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 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 India Mumbai Forecasting Babel, Mukand Singh, Chairperson Clemente, Roberto S., Examination Committee Sutat Weesakul, Examination committee Tawatchai Tingsanchali, Examination committee Thailand (HM King), Scholarship donor Asian Institute of Technology. Thesis ; no. WM-08-05 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B04300 .b12092265 mnait u 240403 0 110314 m a u 0 000:001:PDF:b1209226:000621:0:0:0:0:0:0 Abstract-AIT Thesis no.WM-08-05 n mnait mnarc 22 40 Barcode : 30050120550396 CREATED : 2014-03-31 RECORD # : i12783286 LPATRON : 1028041 LCHKIN : 2018-08-23 # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 4 TOT CHKOUT : 5 TOT RENEW : 15 Barcode : 30050120233217 CREATED : 2016-01-04 RECORD # : i12921300 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 6477 6477 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Thesis no.WM-08-05 30050120550396 2026-08-17 16:28:34 3 2026-08-17 22 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Thesis no.WM-08-05 30050120233217 2026-08-17 16:28:34 2026-08-17 40