000 04333nas a2200469 a 4500
005 20260817162916.0
008 230117s2022 th uu m rtt 0| a1eng d
035 _a.b12396539
099 9 _aAIT Thesis no.WM-22-12
100 1 _aTupaz, Kimberly Torrico
245 1 0 _aApplication of long short-term memory model in predicting hydrologic extremes under climate change and land use change scenarios in the Lancang-Mekong river basin
260 _aPathum Thani, Thailand :
_bAsian Institute of Technology,
_c2022
300 _a99 leaves :
_bill.
490 1 _aThesis;
_vno. WM-22-12
500 _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Water Engineering and Management
502 _aThesis (M. Sc.) - Asian Institute of Technology, 2022
520 _aWith evidence such as warming of the climate system and intensified hydrologic events, climate change continues to be an important subject in the scientific community. It is said that food security and sustenance would be compromised by climate-related extremes for regions with agriculture and fisheries as dominant industries, such as the Lancang-Mekong River Basin. Recent news has shown the worsening drought and flood conditions in the area with the lowest drop of water level in 2019, attributed to both upstream dam politics and climate change. Hence, the assessment of how future extreme hydrologic conditions change is vital. Modern research on deep learning techniques for time series prediction has been made and for this study, Long Short-Term Memory was selected to forecast future flows under climate change SSP2-4.5 and SSP 5-8.5 scenarios. Prior to future projections, historical assessment of hydrologic extremes on selected stations was conducted using Indicators of Hydrologic Alteration (IHA) which demonstrated increasing trend of minimum flows in the lower Mekong region, validated using Mann-Kendall trend test. Bias correction using empirical quantile mapping was done on rainfall and average temperature variables of the 3 GCMs (EC-Earth 3, EC-Earth 3 Veg, and Nor-ESM 2MM to account for biases between observed (i.e., APHRODITE) and simulated values. Approximately, the largest average annual temperature increase is 0.23{u02DA}C/yr and 0.36{u02DA}C/yr under SSP2-4.5 and SSP5-8.5, respectively at Stung Treng station. The resulting median of the 3 GCMs were used as inputs to the LSTM model along with observed flow and land use data. Predicted annual average flows exhibit increasing and decreasing trends for the lower stations (i.e., Pakse and Stung Treng) and upper station (i.e., Chiang Saen), respectively. Further analysis shows increasing minimum flows but decreasing maximum flows.
650 0 _aHydrology
_zMekong River Basin
650 0 _aClimatic changes
_zMekong River Basin
700 1 _aShrestha, Sangam,
_eChairperson
700 1 _aShanmugam, Mohana Sundaram,
_eExamination committee
700 0 _aHo Huu Loc,
_eExamination committee
710 2 _aLuang Prabang Hydroelectric Power/Deedoke Hydroelectric Power Projects,
_eScholarship Donor
710 2 _aAsian Institute of Technology Scholarships,
_eScholarship Donor
810 2 _aAsian Institute of Technology.
_tThesis;
_vno. WM-22-12
856 4 0 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/detail.php?q=B18055
907 _a.b12396539
_bmnait
_cm
902 _a250422
998 _b0
_c230117
_dm
_ea
_fm
_g0
945 _lmnarc
945 _lmnarc
945 _lmnait
945 _lmnait
942 _c40
942 _c67
942 _c20
909 _aBarcode : 30020220004900
_bCREATED : 2023-05-01
_cRECORD # : i13422455
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : -
_bCREATED : 2023-05-01
_cRECORD # : i13422467
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : 30050121067333
_bCREATED : 2024-01-18
_cRECORD # : i13478266
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 1
_iTOT CHKOUT : 0
_jTOT RENEW : 0
909 _aBarcode : 30050121067309
_bCREATED : 2024-01-18
_cRECORD # : i13478278
_dLPATRON : 0
_eLCHKIN : -
_f# RENEWALS : 0
_g# OVERDUE : 0
_hIUSE3 : 0
_iTOT CHKOUT : 0
_jTOT RENEW : 0
999 _c6684
_d6684