000 05468nas a2200445 a 4500
005 20260818114246.0
008 160322s2013 th u m rtt 000 a eng d
035 _a.b12121447
099 9 _aAIT Thesis no.WM-13-25
100 1 _aSirisena, T. A. Jeewanthi Gangani
245 1 0 _aForecasting of long-term seasonal rainfall using artificial neural network :
_ban application to the Ping River Basin, Thailand
260 _aPathum Thani :
_bAsian Institute of Technology,
_c2013
300 _a68 leaves :
_bill.
490 1 _aThesis ;
_vno. WM-13-25
520 _aRainfall is a meteorological phenomena and its variations are link ed with climate con ditions in different region which change the local rainfall pattern. Th e understanding of long term (Seasonal or annual , inter - annual) rainfall variations is important for many sectors like agriculture, industry, health, irrigation, education so on in a region. Ping basin is one of major basin s of upper Chao Phraya in Thailan d. Thailand is under influence of Monsoon and tropical cyclone throughout the year. Present study developed the four seasonal rainfall forecasting models for Ping river basin using Artificial Neural Network (ANN) technique which is a data driven model wide ly used in non - linear analysis and forecast the future seasonal rainfall under climate change scenario . The large scale atmospheric variables (LSAVs) called as predictors for seasonal rainfall (i.e. F eb - Mar - A pr , M ay - J un - J ul , A ug - S ep - O ct and N ov - D ec - J an ) w ere identified using correlation maps. Using correlation analysis and ANN model ing , the most significant variables and their best combinations were determined . It was found best ANN model for each season to forecast the rainfall according to the performan ce indices. Quantile mapping bias correction applied for identified predictors (1971 - 2100) from tw o General Circulation Models (GC Ms); C SIRO Mk3.6 and MPI - ESM - MR, for RCP4.5 scenario. Then forecast the seasonal rainfall till 2100 using developed models. Th e extreme events; wet and dry for next 30 year period (till 2040) was defined according to thresholds at 90 th and 10 th percentile of observed climatology (1971 - 2000). Sea level pressure, surface air temperature, zonal wind, meridian wind, relative humidit y, sea surface temperature and precipitable wa ter were the identified predictors for different lead times (4 - 15 months) with 85 - 95% confident level. The forecast lead time was one month for MJJ a nd FMA seasonal rainfall whereas two month for A SO and NDJ se asonal rainfall. Accordingly, MJJ and NDJ rainfall over Ping basin is forecasted to decrease by 0.5mm and 0.14mm per year respectively from 2011 to 2100. On the other hand ASO and NDJ rainfall increases by 0.45mm and 0.03mm annually during the same period. The Gaussian distribution results shows an increase of extreme wet (above 90 th percentile) by 20% compared to observed period (10%) during monsoon season (ASO) under RCP4.5 scenario from MPI - ESM - MR model. Similarly, approximately 31% and 1 4% probability o f occurrences was forecasted for extreme dry condition during NDJ and FMA seasons respectively under RCP4.5 from CSIRO Mk3.6 model. Results obtained from the study provide understanding of significant variables on rainfall occurrence and fut ure rainfall variations over the Ping river basin. Effective planning and management of resources and implementation strategy can be developed accordingly.
500 _aA thesis submitted in partial fulfillment of the requirements for the Degree of Mas ter of Engineering in Water Engineering & Management
502 _aThesis (M. Eng.) - Asian Institute of Technology, 2013
650 0 _aRain and rainfall
_zPing River Basin (Thailand)
_xForcasting
700 0 _aNkrintra Singhrattna,
_eExamination committee
700 1 _aPerret, Sylvain Roger,
_eExamination Committee
700 0 _aSutat Weesakul,
_eExamination committee
700 1 _aBabel, Mukand Singh,
_eChairperson
710 2 _aAIT Fellowship,
_eScholarship donor
710 2 _aWEM Project,
_eScholarship donor
810 2 _aAsian Institute of Technology.
_tThesis ;
_vno. WM-13-25
856 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/detail.php?q=B04378
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_tAbstract--AIT Thesis no.WM-13-25
_vn
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