Study on displacement prediction of landslide based on neural network

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1 Available online Journal of Chemical and Pharmaceuical Research, 2014, 6(6): Research Aricle ISSN : CODEN(USA) : JCPRC5 Sudy on displacemen predicion of landslide based on neural nework Jian Huang, Zhihuan Liu and Ni Li Hu Bei Universiy of Ars and Science, P. R. China ABSTRACT In recen years, he economic losses caused by landslides were as high as several billion dollars. Therefore, he sudy of landslide risk has become a ho opic oday. Landslide displacemen predicion is a highly nonlinear and exremely complex issue. In mos cases, i is difficul o use mahemaical models o describe he process clearly. Daa mining echnology, which uncovers he hidden daa paerns and models effecively, can be used for landslide displacemen predicion. Series of sysem models are rained, validaed, and applied o a landslide sudy along he Three Gorges and cases from he lieraure. Based on hese monioring daa from deformaion displacemen of he Bazimen landslide in Zigui Couny, naural rainfall in he area of Shazhen and Guizhou, waer level changes of he Three Gorges Dam, we propose a heoreical mehod suiable for analyzing he impac of landslide deformaion. By drawing duraion curve abou each monioring poin displacemen and predisposing facors on he valid daa, we use regression analysis mehod o analyze correlaion and hyseresis of landslide displacemen variaion and predisposing facors hrough he acual siuaion of monioring monhly repors. Key words: neural neworks; spaial daa mining; geological disasers; Regression analysis INTRODUCTION There are many unexpeced geological hazards in recen years such as earhquakes, avalanches, landslides, mudslides, ec. Risk assessmen of geological hazard has become one of he major issues of general ineres [1]. The landslides as a kind of geological disasers, plain and simple, bring varies of serious hreas o humans. Therefore he forecasing of landslide displacemen is very meaningful. Spaial daa mining is a relaively new field wih inegraion of muliple disciplines and a variey of relaed echnologies, including arificial inelligence, daabase, paern recogniion echniques and so on [2-6]. Therefore, spaial daa mining mehods conain no only general daa mining mehods, as well as spaial daabase. There are already many research insiues and researchers ino he field of space research in he daa mining. Hao e al.[7] broken he landslide displacemen ino cycle erms and rend erms, and combined wih he periodiciy characerisics of ime series o analyze cycle iems of landslide displacemen. Dujuan e al.[8] used BP neural nework o predic is displacemen based on he work of Hao. Li Qiang and Li Duan [9] proposed a ime series analysis wih capabiliies of he forecasing complex sysems in developmen rend, and adoped iming analysis mehod o esablish he ARIMA model and he CAR model for landslide displacemen dynamic fore-cass. E.Yesilnacar e al.[10]combined wih logisic regression and neural nework o overcome shorcomings of he saisical mehods ha could no effecively build models of complex geological disasers, deal wih non-linear relaionship beween landslide hazard and predisposing facors. Xiangenjun [11] used rough se o dig ou he inheren law of slope disaser aciviies from he hisorical slope daa. Daifuchu [12] focused on he naural landslide spaial predicion in Hong Kong, adoped wo or single ype o suppor vecor machine for spaial predicion of landslide hazard, and compared wih Logisic regression models a he same ime. 1315

2 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): In addiion, daa mining echnology has grea advanages in erms of analyzing massive amouns of daa. There is a research rend for a comprehensive predicion crierion of daa mining and building forecasing model abou how o choose he righ mehod o inroduce righ heory o he sudy of geological disaser forecas. Wih he diversiy of daa mining and knowledge discovery mehods, researches in he field of geological disaser predicion more use varies of daa mining mehods, compared wih he conclusions in he same research area [13]. Due o early warning command, forecas necessiies and inegraed decision-making, his paper esablishes a mining model suiable for landslide hazard daa, sudies spaial daa mining abou analysis of predicion of landslide hazard and landslide displacemen, supporing geological disaser monioring and early warning and emergency command. To be adoped, such as linear regression, neural neworks and oher daa mining echniques, we process daa auomaically analyzed and summarized o exrac criical daa ha assiss prevenion of geological disasers decisions, conduc a srucural analysis model. Wih monioring repors o analyze he acual experimenal resuls, we verify he reliabiliy and accuracy of he mining model applicaions. A las, according wih final chars, visualizaion, and oher forms of naural language, we esablish a suiable daa mining model for landslide hazard predicion, and presen he knowledge discovery in an easy way for users o undersand. THE NATURE AND CHARACTERISTICS OF THE LANDSLIDE DATA The daa used for experimen is from monioring daa of landslide in area of Zigui couny of he Three Gorges, where belongs o he subropical monsoon climae, abundan rainfall, and more rainsorm, and also owns complicaed geological feaures along he Yangze River. Therefor,i has occurred geological disasers someimes, such as collapses, landslides and debris flows. Bazimen landslide is acive more frequenly in recen years, so his paper chooses his area of landslide o analyze. 1. The propery values of he landslide monioring daa have coninuiy, which is suiable for regression algo-rihm and fiing algorihms o build he model. For as-sociaion rules algorihm in mining algorihms, we mus discreize he landslide daa in advance. 2. The impac of rainfall on landslide displacemens is cumulaive and ime-delay. The generaion of landslide displacemen may be affeced by heavy rainfall in a shor ime, or due o he cumulaive rainfall over a period of ime. 3. Changes in he waer level of he Three Gorges dam are affeced by naural rainfall and he impac of man-made waer drainage aciviies. So, i mus exclude he impac of human aciviy facors when researching he impac of he landslide displacemen on dam waer level. 4. Randomness, insabiliy, and deformaion characerisics of each formaion mechanism of landslides are no all he same, so each landslide mus be a deailed analysis based on he acual siuaion. 5. According o collaion and analysis of he colleced daa abou each landslide in Zigui couny, as well as meeorological daa and hydrological daa, we summarize he following characerisics of he daa. THE DATA MINING APPLICATIONS OF BAZIMEN LANDSLIDE Daa Mining, known as knowledge discovery in daabases (Knowledge Discovery from Daabase), deals wih he observed daa se which is usually large for analysis. Of course, he purpose is o find unknown informaion, summarize and presen he daa in he way ha daa owner can undersand. This paper focuses on he conens of he applicaion of he raw daa for daa mining on geological disaser monioring. Appling he curren daa mining echnology o exrac useful informaion from he hisorical daa, which is beneficial for geological disaser monioring and disaser predicion, ha can provide services for warning command sysem. DATA PREPROCESSING Exising daa is dam waer level daily monioring daa, including wo monioring poins GPS measuremen daa of Bazimen landslide ZG110, ZG111. GPS measuremen daa is a monhly daa which is differen from he rainfall and he waer level of he dam monioring daa in ime granulariy. Therefore, we need preprocessing and re-sampled monhly daa, and selec monh ime by he ime inervals of GPS measuremens, such as dam waer level averages in March 2004 refers ha waer level measuremens beween February 2004 o March in GPS monioring day, namely on February 11, 2004 o March 14. Rainfall daa akes he cumulaive value of he monh. Dam waer level flucuaion rae is used o measure he waer level flucuaions. A posiive value indicaes he waer level rises, a negaive value indicaes he waer level drops. Landslide displacemen rae is he monhly landslide deformaion, which akes he average value of each observaion poin. Afer we organize he raw daa able, in order o prepare for he esablishmen of he model, he daa is normalized o 1316

3 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): eliminae he influence of dimension. Here he daa is normalized o prepare for he laer work wih he neural nework oo. There is an approach we used ha all he daa is convered ino [0,1] or [0.1,0.9] or [-1,1] beween he number o cancel an order of magniude difference beween he dimensional daa, and avoid a large difference in magniude or larger predicion error fi when inpu and oupu daa. In his paper, he experimenal daa wih a ime effec, ha each record mus follow chronologically. When selecing a raining daa se and es daa ses, in accordance wih he ime sequence, selecion of raining daa before eighy, welve afer he prediced daa. Saisfying he above condiions assumed, so i will be is normalizaion. LANDSLIDE DISPLACEMENT FACTOR 1) Displacemen rae and dam waer level and waer level flucuaion rae The average waer level of he Three Gorges dam area changes in seasonal flucuaions. This is he main flood season from May o Sepember every year, when happens o be a year of rainfall. To mee he arrival of flood season, he Three Gorges reservoir releases, he dam average waer level drops. Every year from Ocober o April is he second year sorage period wih less rainfall. Because of he need for power generaion, he reservoir sorages waer, dam waer level rises or remains on average a a cerain waer level. Through he relevan daa analysis from wo monioring poins of he Bazimen landslide, he rend of he cumulaive displacemen was rising a he beginning of sorage waer, bu he rae of increase is growing slowly. As he waer level coninue rising, he displacemen rae ges slower. When he waer level rises o he highes poin, he displacemen rae will ge faser. The waer level is he highes poin of a urning poin. Before and afer he waer level he highes poin, he waer level on he rise and fall jus wo saes, so he decrease in he waer level rises or landslide displacemen ineviably affecs he rae of change. 2) The impac on he waer level of he dam The dam waer level has an effec on seasonal variaion, hus he monhly waer level could effec by before. Here we use ime series of ARMA model o analyze is impac by is own laws. Through repeaedly calculaing, he average waer level of he dam lag facor is 2. Tha is, he dam waer level is affeced by heir firs wo phases daa. The average dam waer level saisfies AR (2), he equaion is below, as seen in Eq. 1. w = w w ε (1) 1 2 In his model, R2 is 0.895, he fi of he model is very high. AIC = , SC = ) The rae of rainfall and displacemen The rainfall of he Bazimen landslide nearby has he seasonal variaion, he landslides and displacemen rae also vary wih rainfall cyclical variaion. Annual rae of landslide displacemens has occurred o accelerae he increase of rainfall during he year. Annual maximum displacemen rae occurred in he monh before and afer he bigges annual rainfall. I indicaes ha here is cerain relevance beween rainfall and landslide displacemen rae, bu he rainfall has a cerain lag effec on landslide deformaion. 4) Is own influence rainfall in he area of Huizhou The rainfall in Guizhou shows seasonal variaion, he monhly rainfall may be affeced by prior rains. Here we use ime series of ARMA model o analyze is impac by is own laws. Through repeaedly calculaing, he average waer level of he dam lag facor is 3. In he model, i = 1, i refers ha making a firs-order differenial conversion on he rainfall daa, hen geing monhly rainfall daa. In he model of IAR (3,1), he final sae in he area has an affecion on heir monhly rainfall daa of he firs hree. Based on above, he subsequen modeling process will consider he impac of he final sae in he curren monh and previous rainfall areas of landslide displacemens. The cumulaive rainfall R1 mees he IAR (3,1), he equaion is below, as seen in Eq. 2. ε (1 B) R1 = ( B B B ) (2) R2=0.999, AIC= , SC= LINEAR REGRESSION In his secion, he relaed inerpreaion includes he average waer level w, dam waer level flucuaion velociy dw, he landslide of Bazimen cumulaive displacemen x1, he landslide of Bazimen average displacemen rae v1, cumulaive rainfall of Guizhou R1, monhly rainfall of Guizhou R1 and heir pre-variables such as dw-2 indicaes 1317

4 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): he waer level flucuaion speed ahead wo phase and so on. Each phase of dam waer level of flucuaion rae equals o he average level of he dam by subracing he average waer level on he phase of he dam, so when he dam waer level flucuaion rae is posiive, said he waer level rises, oherwise he waer level dropped, he absolue value of he speed of dam waer level flucuaion in he period corresponds o he rae of rise or fall. By sepwise regression mehod, he average waer dam, he rainfall of he monh and he former hree monh, waer level flucuaion velociy and he monh before he hree fi following model, he equaion is below, as seen in Eq. 3. x1 = 0.858w 0.438dw dw dw (3) This model is R2 = 0.76, each coefficien of he above equaion is deeced by P. Here Bazimen landslide cumulaive average displacemen is effeced by he dam waer level, he combined effecs of dam waer level flucuaion velociy. The rainfall of Guizhou is no considered among he sae equaion. From he model perspecive, he landslide dam waer level flucuaions in he rae of displacemen wih a negaive correlaion, and is subjec o waer level flucuaions of he firs hree. When he waer level drops, and he drop amoun increases, he landslide displacemen will increase. Here is a comprehensive evaluaion of he resuls of his regression model, as seen in Fig. 1. Each evaluaion of he model is seen in Tab. 1. Fig. 1: Landslide Predicion Curve of Bazimen Tab. 1: Model Evaluaion Value Minimum error Maximum error 0.22 Average error Average absolue error Sandard deviaion Overall fiing accuracy of he linear regression model is 47.25%. This linear regression model for landslide deformaion phase shif, fis effec in genle change par, namely waer displacemen of he landslide deformaion is beer fiing wih accuracy of 51%, marked in Fig. 1. For he displacemen of a large par of he volailiy, i fis landslide displacemen flood ineffecive. fecive. In summary, he linear regression model for landslide deformaion displacemen of waer is appropriae. NEURAL NETWORK METHOD BUILD NEURAL NETWORK MODEL BP neural nework model o predic landslide Bazimen is divided ino four seps: The firs sep is o deermine he srucure of he neural nework; The second sep views observaional daa of landslide displacemen as raining samples and hen normalized; Third sep is nerve nework raining; Fourh sep makes use of he rained neural nework o predic landslide displacemen, and normalizes he inpu daa. Firsly, daa filering mainly used o filer ou some of he source daa able unused variables, and hen samples. Bazimen landslide daa from January in 2004 o November in 2011 has a oal of 92. We chose he former 80 as raining daa, he laer 12 as he predicion daa. Firs feaure selecion before modeling, according o he characerisics of he inpu variables wih predicors variables of he Pearson correlaion coefficien o selec. Pearson coefficien values in [0.9,0.95] for he general imporance, [0.95,1] is imporan. Thus selec he inpu variables of he neural nework. Then ener he firs 80 daa model for raining. Training nework is hree layer nework including inpu layer, a hidden layer and oupu layer. Five neurons in he inpu layer, hree neurons in he hidden layer, one neuron in he oupu layer. The accuracy of 1318

5 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): raining nework is up o 92.66%. Afer esablishing a good neural nework model, he daa inpu he laer welve forecasing daa o predic for Bazimen landslide. There is a sudden increase significanly faser han he monh before and afer he monh in June Excep for he predicive value of he poin, he model for flood displacemen rae is generally beer han he value of waer displacemen. I shows ha he exen of he raining nework o fi he observed daa is no very good, so he overall effec is prediced o be improved. Landslide is a coninuous process, he shape of he variable iself is bound o be affeced by pre-deformaion, which is he ime effec of he landslide. The above model does no consider he effecs of landslides own ime. So here's an aemp o inroduce rend erm of landslide displacemen of he average monhly, and error erm of ime series model o correc he above model. Table 2. The Prediced Resul Average Monhly Rae of Displacemen of Bazimen Landslide Time Average monhly rae of displacemen BP neural nework predicive value Table 3. Esimaion Resuls Minimum error Maximum error Average error Average absolue error Sandard deviaion A lo of he ime series of observed sample will show he rend, seasonal and random, or jus showing hree of he second or one. In his way, you can hink of each ime series, or afer an appropriae ime sequence funcion ransformaion, i can be broken down ino hree pars superimposed. X = T + S + R = 1, 2, L (4) Which { T } is a rend iem, { S } is a seasonal iem, { R } is a random iem. This ime sequence is he superposiion of hree. The average monhly Bazimen landslide displacemen decomposed ino rend erm and seasonal iems, and iems will be added o he above rend BP neural nework model for discussion. There are many ime series decomposiion mehods, such as segmened rend mehod, he rend regression line mehod, quadraic rend, sepwise average mehod, regression, ec., The gradually average mehod is used in his paper. Wih he gradual rend erm average wice sripped ou, he formula is below,he resuls are shown in Fig. 2. U n = 1 6 n i = n 6 y i n 1 T n = U 6 i = n 6 i (5,6) The equaion of Bazimen landslide average displacemen suiable for ARIMA is in Eq. 7. ARIMA(1,1,1): ( B ) (1 B ) x = ( B ) ε + R2=0.996, AIC=567.7, SC= (7) 1319

6 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): Fig 2: Bazimen Landslide Trend Term Average Monhly rae of Displacemen Fig 3: Bazimen Average Displacemen Curve Fiing Landslide The ime series equaions of he landslide displacemens of Bazimen can ge he error erm. The error erm and rend iem were added o he BP nework by order, we ge BP nework wih rend iems, BP nework wih ARIMA and BP nework error erm wih rend erm and he error erm. The four experimenal resuls compared wihin four neural neworks are shown in Table 4. Tab 4: Four Kinds of Neural Neworks Comparison Resuls BP TBP EBP TEBP Training Nework accuracy 92.66% 94.36% 97.03% 96.57% Average error Sandard deviaion The overall predicion accuracy 27.2% 24% 34.8% 38% Flood predicion accuracy 25.71% 20% 37.14% 42.86% From Fig. 4 o Fig. 7 and Tab. 4, he predicion of BP nework wih rend iems and errors iems on Bazimen landslide displacemen is more accurae. Especially, he posiion of exreme poins in Fig. 4, he normal BP nework for his exreme poin generally poorly fi. Afer joining he rend erm and he error erm, he overall fiing accuracy improves in BP nework a lile, he effec is no very obvious improvemen. Bu from he following 4 compares he picure, you can clearly see ha he predicion accuracy of landslide deformaion displacemen of he peak poin of improves a lo. For example, he May in 2011, he predicive value of flood displacemen is 0.194, he observed value is 0.186, he error is only by 4%. The BP nework wih rend erms and erms, in he raining process, he circumsance of fiing in he flood dis-placemen poins is more ypical han BP nework, which is also significanly improved. For example, he error of he prediced value of BP nework in June 2010 up o 47.47%. Afer inroducing rend backward, his error is reduced d o 40%. Afer he inroducion ion of he error erm, he error a ha poin has improved reduces o 9.6%. Afer he inroducion of he error erm and rend erm, he error is reduced o 5%. 1320

7 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): Fig 4: Bazimen Landslide Average Monhly Rae of Displacemen BP Nework Predicion Fig 5: BP Nework Bazimen Landslide Displacemen Rae wih he Average Monhly Trend Term Predicions Fig 6: Bazimen Landslide Average Monhly Rae of Displacemen Error Term BP Nework wih ARIMA Forecasing Resuls Fig 7: BP Nework Bazimen Landslide wih Average Monhly Rae of Displacemen and he Error Term Trend Term Predicions BP nework model wih a rend erm and he error erm for landslide deformaion displacemen sage, drasic change, fis effec, fec, namely flood landslide deformaion displacemen fi beer wih accuracy of 42.86%, as shown in Figure 7 he marked secion. For example, in May 2004, May and July and Augus in 2005, in June and Augus and Sepember in 2006, in July and Augus in 2007, 07, in Sepember 2008, in June and Augus in 2009, in June and July in 2010, in May 2011, he predicion error is less han 10%. For he displacemen of he fla par of he change, i is 1321

8 Jian Huang e al J. Chem. Pharm. Res., 2014, 6(6): fiing badly for waer sorage. In summary, BP nework model wih rend iems and errors iems on he flood landslide deformaion displacemen is more appropriae. CONCLUSION The landslide deformaion displacemen effeced by rock-soil, he naural rainfall and reservoir waer level flucuaions and oher facors. In he field of landslide of landslide displacemen, more researches are o esimae he sabiliy of he landslide and he predicion of landslide deformaion using univariae ime series. In his paper, we akes ino accoun he impac of waer level flucuaions, and oher facors on naural rainfall landslide deformaion hrough a comprehensive analysis, which has beer reflecs he imporan role of exernal incenives for he developmen of landslide deformaion. By using mulivariae regression model, he fiing curve of Bazimen landslide displacemen is beer in he phase of waer sorage, which is closer o he acual rend of displacemen. The fiing resuls of he neural nework are jus is complemenary. Applicaion of BP nework in he predicion of Bazimen landslide displacemen, wih high accuracy of is raining nework, bu he predicion accuracy is no ideal. In order o achieve he purpose of shor-erm forecass, he accuracy of he model remains o be furher improved. Fuure Works In his paper, alhough he characerisics of Bazimen landslide daa o esablish a proper analysis and forecasing model, bu here are many issues o be solved when suiable for all landslide furher. Some observaions due o he landslide displacemen daa poin for he newly added poin, daa monioring less, are no fully uilized. Therefore daa processing could be improved. Since he paricle size is he monhly daa, he accuracy is no enough. If he landslide displacemen of GPS can be accurae o days, he model experimens of landslide deformaion will be more pracical significance, which will play a greaer role in disaser preparedness. Currenly, we only experimen on he Bazimen landslide daa, he follow-up model can be exended o oher landslides in he oher areas. Acknowledgmens This work was financially suppored by Science and Technology Projecs Hubei Provincial Deparmen of Educaion (Projec ID: Q ). REFERENCES [1] Huang,R.Q.,Xiang,X.Q.,Ju,X.X,e al., Geological bullein of china, 23(11): , [2] Marin E, HansPeer K, Jorg S., Spaial Daa Mining: A Lecure Noes in Compuer Science, Springer, [3] Tang,L.B., Research on Progress of Spaial Daa Mining [D]. Graduae School of Chinese Academy of Sciences, [4] Xu,F.,Wang,Y.,Du,Y.,e al., Chinese Journal of Rock Mechanics and Engineering, 30(4):46-751, [5] Wang,L., Geomaics & Spaial Informaion Technology,36(6): , [6] He,B.B.,Cui,Y.,Chen,C.H.,e al, Advances in Earch Science,26(6), [7] Hao,X.Y.,Hao,X.H,Xiong,H.M.,e al, JournalofEngineeringGeology,7(3): , [8] Du,J.,Yin,K.L.,Chai,B., Chinese Journal of Rock Mechanics and Engineering,(09): , [9] Li,Q.,Li,R.Y., Journal of Yangze River Scienific Research Insiue,22(6), [10] E.Yesilnacar, T.Topal. Engineering Geology, 79: , [11] Wang,G.Y.,Cui,H.L.,Li,Q., Rock and Soil Mechanics, 30(8): , [12] Lin,D.C.,An,F.P.,Guo,Z.L.,e al., Rock and Soil Mechanics, 32(1), [13] Jose Z, Jesus P, Juan T. A UML Informaion and Sofware Technolog, 51: ,