AI SearchDevelopment and application of machine learning tools for rainfall forecasting (Record no. 28412)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05528nas a2200493 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260818090534.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200907s2019 th m rtt 000 a eng d |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b12313889 |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT Thesis no.WM-19-14 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Le Ngoc Hieu |
| 245 10 - TITLE STATEMENT | |
| Title | Development and application of machine learning tools for rainfall forecasting |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 155 leaves : |
| Other physical details | ill. (some col.) + |
| Accompanying material | 1 online resource |
| 490 1# - SERIES STATEMENT | |
| Series statement | Thesis; |
| Volume/sequential designation | no. WM-19-14 |
| 500 ## - GENERAL NOTE | |
| General note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Eng.) - Asian Institute of Technology, 2019 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Because of the complexity of the atmospheric processes that can generate precipitation with variety of different factors over a wide range of scales both in space and time, rainfall can be considered to be one of the most complicated and unprecedented factors in the hydrology cycle to understand. Therefore, meteorologists have long been developing number of mathematical models in attempt to adapt with atmospheric dynamics which is extremely complicated. For the case of Thailand, heavy rainfall often occurs in Central and Northeast parts which can possibly lead to flood. Hence, this study aims to develop machine learning as a tool to serve as an early warning system that can support Weather Research and Forecasting - Regional Ocean Modelling System (WRF - ROMs) to increase the accuracy of prediction and detect extreme events. The study first selected the best qualified stations that met three specific criteria among more than 300 telemetering stations from Hydro Informatics Institute. After that, three different points that were nearest to station's coordinates were extracted from Weather Research and Forecasting - Regional Ocean Modelling System. Next, Pearson correlation test was performed to decide input features for the model. As a result, twelve stations were selected among more than three hundred stations and Pearson correlation tests indicated high correlation between variables in telemetering stations and low or no correlation between telemetering stations and WRF - ROMs model's outputs. Therefore, input features were included both station and WRF - ROMs data. Machine learning model for rainfall forecast was built using the concept of Decision tree with Adaptive boosting (Decision tree with Adaboost). Precipitation was forecasted based on two different concepts which are rain and no - rain conditions and multiples levels of rain. Each concept was based on different threshold in order to replace numeric precipitation with binary features. The results indicated better performance of rain and no - rain conditions over multiple levels of rain with a few stations has excellent accuracies. Prediction of multiple levels of rain were lower in accuracy, however, the model showed its capability capturing extreme events. Since machine learning needs input to generate output, future inputs were calculated based on their lagged features. Feature inputs were station temperature, humidity and pressure. Lagged temperature, humidity and pressure were the inputs to calculate future temperature, humidity and pressure. Furthermore, autocorrelation test was used to determine to best lagged hour that had the highest correlation to the current feature. To forecast future inputs, machine learning with concepts of Decision tree with Adaboost and Polynomial regression were used. As a result, lagged two hours of each feature has better correlation to the current feature than lagged three hours and onwards. Predicting temperature, humidity and pressure in two hours ahead had higher confidence than predicting them in three hours ahead. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| General subdivision | Development |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Rain and rainfall |
| General subdivision | Forecasting |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Monsoons |
| Geographic subdivision | Thailand |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sutat Weesakul, |
| Relator term | Chairperson |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Shrestha, Sangam, |
| Relator term | Examination Committee |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sarawut Ninsawat, |
| Relator term | Examination committee |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Somchai Chonwattana, |
| Relator term | Examination committee (External Expert) |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Kanoksri Sarinnapakorn, |
| Relator term | Examination committee (External Expert) |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | AITCV Silver Anniversary Scholarships, |
| 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-19-14 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B06380">http://203.159.5.9/ait-thesis/detail.php?q=B06380</a> |
| 907 ## - LOCAL DATA ELEMENT G, LDG (RLIN) | |
| a | .b12313889 |
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| a | 250928 |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) | |
| Operator's initials, OID (RLIN) | 0 |
| Cataloger's initials, CIN (RLIN) | 200907 |
| First date, FD (RLIN) | m |
| -- | a |
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| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnait |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnait |
| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
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| 945 ## - LOCAL PROCESSING INFORMATION (OCLC) | |
| l | mnarc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 40-Archives |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 61-CD-ROM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | 20-AIT Publication |
| 909 ## - LOCAL ITEMS USED | |
| Barcode | Barcode : 30050120878292 |
| CREATED | CREATED : 2019-06-19 |
| RECORD Id | RECORD # : i13194227 |
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| Barcode | Barcode : 30050120979652 |
| CREATED | CREATED : 2020-07-09 |
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| CREATED | CREATED : 2020-07-09 |
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| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type | Cost, normal purchase price | Copy number |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Thesis no.WM-19-14 | 30050120878292 | 18/08/2026 | 18/08/2026 | 40-Archives | ||||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Thesis no.WM-19-14 | 18/08/2026 | 18/08/2026 | 61-CD-ROM | |||||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | AIT Publications | 18/08/2026 | AIT Thesis no.WM-19-14 | 30050120979652 | 18/08/2026 | 18/08/2026 | 20-AIT Publication | 50.00 | 1 | ||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | AIT Publications | 18/08/2026 | AIT Thesis no.WM-19-14 | 30050120979645 | 18/08/2026 | 18/08/2026 | 20-AIT Publication | 50.00 | 2 |