Ӭng dөQJFiFSKѭѫQJSKiS[iFVXҩt thӕng kê gӗm tӹ sӕ tҫn suҩt FR, chӍ sӕ thӕng kê SI, trӑng sӕ chӭng cӭ WoE và hӗi quy logistic LR tích hӧp vӟi GIS Phҫn mӅm Ilwis mã nguӗn mӣ ÿӇ thành lұp các
Trang 3i
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7iFJLҧ[LQFDPÿRDQÿk\OjF{QJWUuQKQJKLrQFӭXFӫDEҧQWKkQWiFJLҧ&iFNӃWTXҧQJKLrQFӭXYjFiFNӃWOXұQWURQJOXұQiQQj\OjWUXQJWKӵFYjNK{QJVDRFKpSWӯEҩWNǤPӝWQJXӗn nào, GѭӟLEҩWNǤKuQKWKӭFQjR9LӋFWKDPNKҧRFiFQJXӗQWjLOLӋX QӃXFy ÿmÿѭӧFWKӵFKLӋQWUtFKGүQYjJKLQJXӗQWjLOLӋXWKDPNKҧRÿ~QJTX\ÿӏQK
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Trang 4Mӝt mô hình sӕ JKpSÿ{LSKkQWtFKWKҩm-әQÿӏQKÿѭӧc sӱ dөQJÿӇ mô phӓng thҩm và әQÿӏnh mái dӕFGѭӟLFiFÿLӅu kiӋQP{LWUѭӡQJÿһFWUѭQJQKѭWtQKWKҩPFѭӡQJÿӝ PѭD
và cҩu tҥo hình hӑc mái dӕc cho vùng nghiên cӭX3KѭѫQJSKiSSKҫn tӱ hӳu hҥQÿѭӧc
sӱ dөng trong phân tích thҩm, kӃt quҧ cӫa sӵ WKD\ÿәi áp lӵFQѭӟc lӛ rӛQJkPÿѭӧc sӱ dөng phân tích әQÿӏnh mái dӕc bҵQJSKѭѫQJSKiSFkQEҵng giӟi hҥn Morgenster-Price Nhӳng mӕi quan hӋ giӳa hӋ sӕ an toàn mái dӕc YjFѭӡQJÿӝ PѭDKӋ sӕ thҩm, góc dӕc, chiӅu cao mái dӕFÿѭӧc rút ra
KӃt quҧ phân tích cho thҩy hӋ sӕ thҩm có vai trò quan trӑQJÿӕi vӟi sӵ әQÿӏnh cӫa mái dӕc Khi thӡLJLDQPѭDNpRGjLKѫQQJj\KӋ sӕ DQWRjQWKD\ÿәi nhiӅu nӃu mái dӕc cҩu tҥo bӣLÿҩt có hӋ sӕ thҩm nhӓ YjtWWKD\ÿәLKѫQNKLPiLGӕc cҩu tҥo bӣLÿҩt có hӋ
sӕ thҩm lӟn HӋ sӕ DQWRjQWKD\ÿәi theo thӡi gian GѭӟLFiFFѭӡQJÿӝ Pѭa khác nhau,
hӋ sӕ an toàn càng giҧPNKLFѭӡQJÿӝ PѭDFjQJOӟn 1Jѭӥng FѭӡQJÿӝ PѭDmái dӕc phá hӫy phө thuӝc thӡLJLDQPѭD CѭӡQJÿӝ PѭDÿҥt 10 mm/h, sau thӡLJLDQKѫQQăP ngày mái dӕc mҩt әQÿӏnh
Ӭng dөQJFiFSKѭѫQJSKiS[iFVXҩt thӕng kê gӗm tӹ sӕ tҫn suҩt (FR), chӍ sӕ thӕng kê (SI), trӑng sӕ chӭng cӭ (WoE) và hӗi quy logistic (LR) tích hӧp vӟi GIS (Phҫn mӅm Ilwis mã nguӗn mӣ ÿӇ thành lұp các bҧQÿӗ dӵ báo tai biӃQWUѭӧt lӣ ÿҩt huyӋn Khánh 9ƭQKWӍnh Khánh Hòa
Trang 5iii
&iFÿLӇPWUѭӧt lӣ ÿm[ҧy UDÿѭӧc thu thұp và lұp thành bҧQÿӗ phân bӕ không gian các ÿLӇPWUѭӧt lӣ ÿLӇPWUѭӧt lӣ) Các yӃu tӕ ҧQKKѭӣng ÿӃQWUѭӧt lӣ gӗm (11 yӃu tӕ): FDRÿӝ, góc dӕFKѭӟng dӕc, chӍ sӕ ҭPѭӟWÿӏDKuQKÿӝ uӕn cong bӅ mһt mái dӕc, thҥch hӑc, khoҧQJFiFKÿӃQÿѭӡng giao thông, khoҧQJFiFKÿӃn sông suӕi, khoҧQJFiFKÿӃn ÿӭt gãy, chӍ sӕ thӵc vұWYjOѭӧQJPѭDOӟn nhҩWQăP%ҧQÿӗ trӑng sӕ các yӃu tӕ ҧnh KѭӣQJÿѭӧc thành lұp dӵa vào mӕi liên quan không gian giӳa các yӃu tӕ ҧQKKѭӣng và phân bӕ FiFÿLӇPWUѭӧt lӣ theo ciFSKѭѫQJSKiS)56,:R(Yj/5
BҧQÿӗ dӵ báo tai biӃQWUѭӧt lӣ ÿѭӧc thành lұp bҵng viӋc chӗng lӟp các bҧQÿӗ trӑng sӕ các yӃu tӕ ҧQKKѭӣQJYjÿѭӧc chuҭQKyDÿӇ có phân bӕ chuҭn hoһc gҫn phân bӕ chuҭn
Áp dөQJSKѭѫQJSKiSÿӝ lӋch chuҭn (Standard Deviation Classification) chia bҧQÿӗ chӍ sӕ tai biӃQWUѭӧt lӣ làm 5 phân vùng vӟi các mӭFÿӝ tai biӃQWUѭӧt lӣ khác nhau: rҩt thҩp, thҩp, trung bình, cao và rҩt cao
Sӱ dөQJÿѭӡng cong Success rate ÿiQKPӭFÿӝ phù hӧp và ÿѭӡng cong Prediction rate ÿiQKJLi ÿӝ chính xáFFiFSKѭѫQJSKiS)56,:R(Yj/5*LiWUӏ cӫa phҫn diӋn tích ErQGѭӟLFiFÿѭӡng cong (Areas Under Curves - $8& ÿѭӧc sӱ dөQJQKѭPӝt thông sӕ ÿӏQKOѭӧQJÿӇ kiӇm chӭQJFiFSKѭѫQJSKiS.Ӄt quҧ cho thҩ\FiFSKѭѫQJSKiSQj\FymӭFÿӝ phù hӧSYjÿӝ chính xác cao (AUC = 0,8~0,9)
Thuұt toán mô hình trung bình Bayesian (BMA) cӫa phҫn mӅm thӕQJ Nr 5 ÿѭӧc áp dөQJÿӇ [iFÿӏnh các yӃu tӕ ҧQKKѭӣng nhҩt và các mô hình tӕLѭXWә hӧp yӃu tӕ ҧnh KѭӣQJÿӕi vӟLWUѭӧt lӣ KӃt quҧ:
x Tám yӃu tӕ ҧQKKѭӣQJOLrQTXDQÿӃQWUѭӧt lӣ: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡng giao WK{QJOѭӧQJPѭDOӟn nhҩWQăPJyFGӕc, thҥch hӑc, khoҧng cách ÿӃQÿӭt gãy, khoҧng FiFKÿӃn sông suӕLYjKѭӟng dӕc
x Bӕn yӃu tӕ ҧQKKѭӣng quan trӑng nhҩt: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡng giao thông, OѭӧQJPѭDOӟn nhҩt QăPYjJyFGӕc
x 1ăP mô hình tӕLѭXWә hӧp yӃu tӕ ҧQKKѭӣng:
Mô hình 1: cDRÿӝ, khoҧQJFiFKÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP
và góc dӕc
Trang 6Mô hình 5: cDRÿӝ, khoҧng cách ÿӃQÿѭӡQJJLDRWK{QJOѭӧQJPѭDOӟn nhҩWQăP
góc dӕc, khoҧQJFiFKÿӃn sông suӕi
Trong 5 mô hình tӕLѭX, ÿiQKJLiWKHRhiӋu quҧ dӵ báo thì mô hình 1 là mô hình phù hӧp nhҩt vì sӱ dөng ít yӃu tӕ ҧQKKѭӣQJÿӇ dӵ báo (4 yӃu tӕ), ÿiQKJLiWKHRÿӝ chính xác dӵ báo thì mô hình 3 là mô hình phù hӧp tӕt nhҩt
PKѭѫQJSKiS:R(OjSKѭѫQJSKiSGӵ báo tӕt nhҩt YuFyÿӝ chính xác cao nhҩt, tiӃSÿӃn OjFiFSKѭѫQJSKiS)56,Yj/5 3KѭѫQJSKiS)5Yj6,WX\Fyÿӝ chính xác thҩSKѫQQKѭQJÿѫQJLҧn trong tính toán nên cҫQ[HP[pWÿӇ áp dөng
Trang 7A numerical model of analysis coupled seepage-stability used to simulate the seepage and slope stability under conditions of specific environment such as soil permeability, rainfall intensity, water table location and slope geometry in the study area Finite element method used in seepage analysis, results of change of negative pore water pressure then used in the slope stability analysis to calculate the safety factor by the application of the limit equilibrium Morgenstern and Price slope stability method The relationships between safety factor and rainfall intensity, soil permeability, angle slope, high slope were identified
Result in analysis suggesting coefficient of permeability have an important role for the stability of the slope When the rainy period is more than 3 days, the factor of safety will change much if the slope formed by soil having small coefficient of permeability and little change when the slope formed by soil having large coefficient of permeability The factor of safety decreases more as the greater rainfall intensity Threshold of rainfall intensity cause the failure of the slopes depending on the rainy period At the rainfall intensity of 10 mm/h, after a period of time more than five days, the slope will be destabilized
Applying statistical probability approaches, including frequency ratio (FR), statistical index (SI), weights of evidence (WoE) and logistic regression (LR) methods integrated with GIS analytical tools to produce landslide hazard maps in Khanh Vinh district, Khanh Hoa province
Trang 8vi
A landslide inventory map identifies the definite and probable areas of existing landslides (231 landslides), and is the most basic requirement for a landslide hazard assessment The product of a landslide inventory map is a spatial distribution of landslides as points or to scale Landslides-related factors chosen primarily upon available data and experiences of experts to the study area In this study, there are eleven landslide-related factors were chosen: elevation, slope angle, slope direction, topographical wetness index, slope shape, lithology, distance from road, distance from drainage, distance from fault, normalized difference vegetation index and maximum precipitation in year The weight maps of the influence factors were established based
on the spatial relationship between the influencing factors and the distribution of existing landslides by FR, SI, WoE and LR methods
Landslide hazard maps were established upon the combination of the weight maps of the influence factors The landslide hazard index before dividing the landslide hazard zones should be standardized for histograms having normal distribution or near normal distribution Standard Deviation method is used to divide the landslide hazard map into different landslide hazard zones In this study, there are 5 levels of landslide hazard zone: very low, low, moderate, high and very high
Using the success rate curve and prediction rate curve assess the fit and accuracy of FR,
SI, WoE and LR approaches The value of the area under these rate curves (AUC) was used as a quantitative parameter to validate the method The results show that these approaches has the goodness of fit and the high accuracy (AUC = 0.8 ~ 0.9)
Bayesian Model Average (BMA) of the R statistical software was applied to identify the most influential factors and the combinatorial optimization models of landslide-related factors
x There are eight landslide-related factors: elevation, distance from road, maximum precipitation in year, slope angle, lithology, distance from fault, distance from drainage and slope direction
x There are four the most important landslide-related factors: elevation, distance from road, maximum precipitation in year and slope angle
Trang 9vii
x There are five combinatorial optimization models of landslide-related factors:
Model 1: elevation, distance from road, maximum precipitation in year and slope
angle
Model 2: elevation, distance from road, maximum precipitation in year, slope angle
and lithology
Model 3: elevation, distance from road, maximum precipitation in year, slope angle,
lithology and distance from fault
Model 4: elevation, distance from road, maximum precipitation in year, slope angle
and distance from fault
Model 5: elevation, distance from road, maximum precipitation in year, slope angle
and distance from drainage
In five the optimal models, evaluated according to the level of forecasting efficiency, model 1 is the most optimization model due to using the least landslide-related factor (four factors); evaluated according to the level of forecasting accuracy, model 3 is the best optimization model
WoE method is the best forecasting method for highest accuracy, followed by FR, SI and LR FR and SI methods have slightly lower accuracy but the calculation is simple Therefore, FR and SI methods should be considered for application in landslide hazard prediction
Trang 10viii
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Tác giҧ xin bày tӓ lòng biӃWѫQVkXVҳFÿӃn thҫ\3*676ĈұX9ăQ1Jӑ, thҫy TS Tҥ QuӕF'NJQJÿmTXDQWkPJL~Sÿӥ, tұQWuQKKѭӟng dүn và cung cҩp tài liӋu tham khҧo
giúp tác giҧ hoàn thành luұn án tiӃQVƭ
Tác giҧ xin trân trӑng cҧPѫQTXêWKҫy cô giҧng viên Bӝ P{QĈӏa kӻ thuұW.KRDĈӏa chҩt và DҫXNKtÿmQKLӋt tình giҧng dҥ\YjJL~Sÿӥ tác giҧ trong thӡi gian làm luұn án tiӃQVƭ
Chân thành cҧPѫQ7UѭӡQJĈҥi hӑF%iFKNKRD7S+&03KzQJĈjRWҥRVDXÿҥi hӑc, Khoa Kӻ thuұWĈӏa chҩt và Dҫu khí, Bӝ môQĈӏa kӻ thuұt, Phòng thí nghiӋPĈӏa kӻ thuұWÿmWҥo mӑLÿLӅu kiӋn thuұn lӧi cho tác giҧ hoàn thành nhiӋm vө
Trang 11ix
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2.5.6 7UҫPWtFKV{QJKLӋQÿҥL D42 ) 21
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2.5.10 7UҫPWtFKV{QJ- ONJWtFK DS4 22
ĈLӅXNLӋQÿӏDFKҩt công trình 22
2.6.1 3KӭFKӋFiW- FXӝLVӓLQJXӗQJӕFWUҫPWtFKV{QJ- ĈӋWӭ 22
2.6.2 3KӭFKӋÿҩWORҥLVpWFKӭDGăPVҥQÿDQJXӗQJӕF- ĈӋWӭ 22
2.6.3 3KӭFKӋWUҫPWtFKOөFQJX\rQ-XUDJLӳDYj&UHWDWUrQ 22
Trang 12x
2.6.4 3KӭFKӋSKXQWUjR-XUDWUrQYj&UHWD 23
2.6.5 3KӭFKӋ[kPQKұS&UHWD- Paleogen 23
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