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Forecasting of long-term seasonal rainfall using artificial neural network : (Record no. 46460)

MARC details
000 -LEADER
fixed length control field	05468nas a2200445 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20260818114246.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	160322s2013 th u m rtt 000 a eng d
035 ## - SYSTEM CONTROL NUMBER
System control number	.b12121447
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number	AIT Thesis no.WM-13-25
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name	Sirisena, T. A. Jeewanthi Gangani
245 10 - TITLE STATEMENT
Title	Forecasting of long-term seasonal rainfall using artificial neural network :
Remainder of title	an application to the Ping River Basin, Thailand
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	Pathum Thani :
Name of publisher, distributor, etc.	Asian Institute of Technology,
Date of publication, distribution, etc.	2013
300 ## - PHYSICAL DESCRIPTION
Extent	68 leaves :
Other physical details	ill.
490 1# - SERIES STATEMENT
Series statement	Thesis ;
Volume/sequential designation	no. WM-13-25
520 ## - SUMMARY, ETC.
Summary, etc.	Rainfall 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 ## - GENERAL NOTE
General note	A thesis submitted in partial fulfillment of the requirements for the Degree of Mas ter of Engineering in Water Engineering & Management
502 ## - DISSERTATION NOTE
Dissertation note	Thesis (M. Eng.) - Asian Institute of Technology, 2013
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Rain and rainfall
Geographic subdivision	Ping River Basin (Thailand)
General subdivision	Forcasting
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Nkrintra Singhrattna,
Relator term	Examination committee
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name	Perret, Sylvain Roger,
Relator term	Examination Committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Sutat Weesakul,
Relator term	Examination committee
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name	Babel, Mukand Singh,
Relator term	Chairperson
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element	AIT Fellowship,
Relator term	Scholarship donor
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element	WEM Project,
Relator term	Scholarship donor
810 2# - SERIES ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element	Asian Institute of Technology.
Title of a work	Thesis ;
Volume/sequential designation	no. WM-13-25
856 ## - ELECTRONIC LOCATION AND ACCESS
Materials specified	Full-Text
Uniform Resource Identifier	<a href="http://203.159.5.9/ait-thesis/detail.php?q=B04378 ">http://203.159.5.9/ait-thesis/detail.php?q=B04378 </a>
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Cataloger's initials, CIN (RLIN)	160322
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945 ## - LOCAL PROCESSING INFORMATION (OCLC)
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	20-AIT Publication
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	40-Archives
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Holdings
Not for loan	Home library	Current library	Shelving location	Date acquired	Cost, normal purchase price	Full call number	Barcode	Date last seen	Copy number	Price effective from	Koha item type
Available for Loans	Asian Institute of Technology Library	Asian Institute of Technology Library	AIT Publications	18/08/2026	50.00	AIT Thesis no.WM-13-25	30050120690861	18/08/2026	1	18/08/2026	20-AIT Publication
Available for Loans	Asian Institute of Technology Library	Asian Institute of Technology Library	AIT Publications	18/08/2026	50.00	AIT Thesis no.WM-13-25	30050120690879	18/08/2026	2	18/08/2026	20-AIT Publication
Available for Loans	Asian Institute of Technology Library	Asian Institute of Technology Library	Archives	18/08/2026		AIT Thesis no.WM-13-25	30050120229983	18/08/2026		18/08/2026	40-Archives

Forecasting of long-term seasonal rainfall using artificial neural network : (Record no. 46460)

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Asian Institute of Technology Library