Zuihai Li * School of Continuing Education, Yulin University, Yulin , Shaanxi, China *Corresponding author(

Transcription

1 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 do: / Ecologcal Model of Groundwater Envronment Based on Hybrd Soft Computng Method Zuha L * School of Contnung Educaton, Yuln Unversty, Yuln , Shaanx, Chna *Correspondng author(e-mal: lzuha@163.com) Feng Zhang School of Informaton Engneerng, Yuln Unversty, Yuln , Shaanx, Chna Abstract For mnng groundwater ecosystems hghly complex and non-lnear, an ecologcal model of groundwater envronment based on hybrd soft computng method (GE_HSC) was establshed from the pont of vew of algorthm optmzaton, and t s ncludes the dfference evoluton algorthm and the neural networ module. The expermental results show that there s no sgnfcant postve correlaton between the correlaton of the samples and the predcton accuracy, whle the networ structure and the representaton of the samples have an mportant nfluence, and the water resources, envronment and economy n Yuln are n uncoordnated stage at present, the economc development s faster, but the water resources are relatvely poor, and economcs development of the envronment caused great destructon,mnng area water polluton s more serous. Key words: Hydronformatc, Water Ecologcal, Hybrd Soft Computng, BP Neural Networ. 1. INTRODUCTION Groundwater resources n Yushen mnng area, coal resources are relatvely scarce, unreasonable explotaton caused enormous damage to groundwater resources, a serous threat to the sustanable development of the area, dynamc regulaton of water resources s an effectve way to allevate the problem. The area s located n the Loess Plateau of Northern Shaanx and Maowusu Desert border areas, lac of water resources, a fragle ecologcal envronment, the explotaton of coal resources, large-scale destructon of the ecologcal envronment of groundwater resources and coal resources n an ntegrated system, put forward the urgent demand for groundwater protecton and control (Zhang and Wang, 2014). But so far, no protecton and regulaton of a comprehensve dynamc control model can support the groundwater resources effectvely, greatly restrcts the mnng area groundwater resources protecton, to acheve a breathrough n the basc theory of related aspects. Groundwater s an mportant part of water resources crculaton system; groundwater dynamcs s not only affected by the hydrogeologcal condtons of aqufer tself, but also affected by the external envronment. In recent years, more and more scholars use soft computng methods to study the complex water envronment smulaton problems (Bao and Fang, 2007). Soft computng method does not requre pror nowledge, or qualtatve emprcal nowledge as a reference, from a large number of montorng data, access to the dynamcs of the system law, so as to mae up for the human understandng of the evoluton of ecosystems on the defcences (Pnho and Ferrera, 2015). At the method level, soft computng manly ncludes artfcal neural networ, fuzzy mathematcs, genetc algorthm, support vector machne and so on. By usng hybrd soft computng method, the nowledge, technology and method of dfferent sources can be combned to adjust the structure and parameters of the system to adapt to the changng envronment. To accurately evaluate the groundwater recharge and recharge resources s the analyss of the hydrologcal cycle law and reasonable bass for water resources plannng and sustanable groundwater explotaton plan (L and Zhang, 2016); t has very mportant strategc sgnfcance for the sustanable development of the local economy and socety, especally n ard and sem-ard water shortage area. Therefore, choose the hgh credblty of the smulaton method to evaluate the groundwater recharge, to reveal the temporal and spatal varaton of groundwater recharge and mprove the theory and method of groundwater resources evaluaton, has mportant theoretcal and practcal sgnfcance. Ths paper studes some soft computng methods such as artfcal neural networ, fuzzy mathematcs, genetc algorthm and support vector machne, and then study the mxed form of them, a smulaton model of groundwater envronment hybrd soft computng method based on the establshed, then the model s appled to the ecologcal envronment of groundwater smulaton n Yushen mnng area. 2. THEORY AND METHOD OF HYBRID SOFT COMPUTING Soft-computng s proposed by Zadeh, founder of Fuzzy Set Theory n 1994, whose gudng prncple s to develop tolerance technques for uncertanty, mprecson and ncomplete authentcty to obtan easy processng, 369

2 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 hgh robustness, Low cost and good ntegraton of the actual method (L, 2015). Soft computng s for hard computng, the tradtonal computng (hard computng) s the man features of strct, accurate and accurate. But hard computng s not sutable for dealng wth many problems n real lfe, and soft computng can acheve lowcost solutons and robustness. At the method level, soft computng manly ncludes fuzzy mathematcs, artfcal neural networ, genetc algorthm, support vector machne and so on. Soft computng s a very wde area, the model manly ncludes cellular automata, ndvdual based and box based, and these modes are manly dscrete n the system varables, tme doman and spatal doman compared wth the tradtonal ensemble model (Ian and Inyang, 2010), we tae ndvdual or spatal unts as objects, to study ther temporal evoluton and spatal movement n order to obtan the spatal and temporal pattern of the system. The representatve of soft computng ncludes artfcal neural networ, fuzzy mathematcs, genetc algorthm, genetc programmng, and chaos theory and law method. Compared wth the usual conceptual methods, these methods tae qualtatve emprcal nowledge as reference and obtan the dynamc laws of the system from a large number of montorng data, thus mang up for the defcency of human understandng of the evoluton mechansm of ecosystem. Artfcal neural networ (ANN) s a branch of soft computng, (M-P model) proposed by Mc Culloch and Ptts n 1943 (Booth, 1986), whch s a mathematcal model of formal neuron structure. Neurons are the cells n the nervous system that are drectly nvolved n nformaton recepton or generaton, transmsson and processng of nformaton. The ntellgence of the human bran s realzed by a complex networ of nformaton flowng through neurons. The basc unt of operaton of artfcal neural networ s a neuron, each neuron has a specfc nternal functon operaton, under normal crcumstances, the neuron s a mult-nput, sngle output nonlnear component, p s the nput of neurons, the ntensty of the nformaton mpact s measured by the jont weght,we can through the actvaton of the neuron functon f to get the output value, the fnal output of the neuron s determned by comparng t wth the threshold value n the output value. The relatonshp between neuron nput and output s descrbed by equaton (1): ( ) n y t f. p ( t ) (1) 1 Where y( t) the neuron s output; f (.) s the neuron actvaton functon; s the jon weght; p s the nput nformaton; s threshold. Dfferental Evoluton (DE) (Rah, 2011) s a relatvely new soft computng evolutonary algorthm; ts overall structure s smlar to genetc algorthm (GA). The man dfference between genetc algorthms and evolutonary s operatons dfference. Evolutonary algorthm evoluton process s as follows: (1) Constructng the dfference degree vector D ( x x ) ( x x ),( x, x, x, x ) s a randomly selected four ndvduals n the populaton, usng abcd a b c d a b c d D abcd to mpose nose on optmal ndvduals g x or random ndvduals, resultng n a varant x xg F. D abcd. (2) Selectng the cross-factor R, a random nteger rnbr( ) on [1, n ] s generated for each x ( x 1, x 2,..., x n ) of a populaton, and then x and x ntersect to produce a new ndvdual v. x j, f ( rand( j) R) vj ( t 1) xj, f ( rand( j) R) (2) Progeny v generated by x and x j compete wth the parent for ndvdual x, acceptng good ndvduals and replacng the poorer ndvduals wth the same populaton sze. 3. WATER ENVIRONMENT PREDICTION AND EVALUATION Neural networ and evolutonary computaton methods, have a strong adaptve, self-learnng ablty and search capabltes, but each has ts own shortcomngs, Neural networ usng gradent descent method, good at local optmal soluton search, evolutonary algorthm usng random methods, good at global optmal soluton search, the combnaton of these two types of applcatons become a trend. The combnaton of dfferent evolutonary algorthms and neural networs has attracted more and more attentons. The research n ths feld s very actve and has made a lot of achevements. It has brought a prospect for the evolutonary computaton and the applcaton of neural networ. In ths paper, DE-BP artfcal neural networ s establshed by usng DE algorthm and neural networ(quroga and Popescu,2013). DE algorthm searches the ntal jont weghts from the global pont of vew for the neural networ, and the neural networ contnues to search for the optmal soluton usng the BP algorthm untl the requred soluton s found. DE-BP algorthm manly uses the global 370

3 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 search capablty of DE algorthm(bagrov and Barton,2013), avods the "premature" phenomenon n BP algorthm tranng process and falls nto the local optmal soluton, and optmzes the neural networ structure to desgn the neural networ wth the optmal topology structure. The basc BP algorthm conssts of two aspects(yuan Xu and Hufeng Xue,2016): the forward propagaton of the sgnal and the nverse propagaton of the error, e, the calculaton of the actual output n the drecton from nput to output, whle the weght and threshold of the correcton from the output to the nput drecton, as s shown n Fgure 1. x 1 o L x j x M o Fgure 1. DE-BP networ structure dagram BP algorthm s one of the most mature tranng algorthms for neural networ tranng because of ts smple, easy to do, small amount of computaton and strong parallelsm. However, BP has low learnng effcency, slow convergence speed and easy to fall nto local mnmum state. Due to the lac of hstorcal data, the collected data samples are lmted. Based on the shortcomngs of BP neural networ and the lmted envronmental sample data n Yuln area, BP neural networ and DE algorthm are used to optmze the learnng of BP networ, whch can accelerate the convergence and avod the local mnmum Has certan effect, and ts change tendency forecast. In Fgure 1, the nput The output net of the node of the hdden layer s calculated as follows: M net v x (3) j j j 1 y of the node of the hdden layer s calculated as follows: y M f net f vj x j (4) j 1 The nput net of the node of the output layer s calculated as follows: The output o of the node of the output layer s calculated as follows: q q M net w y w f vj x j (5) 1 1 j1 q M o f net f w f vj x j (6) 1 j1 In ths paper, a set of data (( t0, f0),( t1, f1),...,( tn, f n)) for a certan envronmental ndex x s establshed as follows: the functon approxmaton neural networ model wth sngle hdden layer: f v t b x t, D ( v ) n 0 (0) a (7) 371

4 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 Let D be a postve real varable, for 0,1,..., n, set 1 x 0.5( x 1) ( x x ) x. n max mn mn a, ( ) t 3 2 t 5 t e, 4. SIMULATION AND APPLICATION FOR WATER ECOLOGICAL ENVIRONMENT Groundwater ecosystem s a complex system wth mult - factor couplng, the relatonshp between ecologcal factors s complex, showng great randomness, uncertanty and nonlnearty (Wen-Sheng and Hu- Feng, 2014). The nteracton between the varous factors n the system and ts dynamc change process are not fully nown, whch restrcts the development of determnstc ecologcal hydrodynamcs. Based on the data optmzaton, ths paper establshes a smulaton model of groundwater envronment based on hybrd soft computng method (GE_HSC). The basc framewor of the GE_HSC model s shown n Fgure 2. Fgure 2. GE_HSC model framewor The model s a mult-nput sngle-output model, usually the model nput s a number of groundwater envronmental ndcators, and the model output s the water qualty ndex data. The role of neural networ and support vector machne n the model s smlar, the relatonshp s ted, and we can choose one of them. In ths paper, a montorng area of groundwater n Yuln cty was chosen as the object of study. The data of the model establshment and verfcaton were measured by the measured data. Yuln cty has 12 watershed zonng, n order to measure the synchronous seres calculaton of the average total of bllon m 3 rver s runoff, and the annual runoff of 50%, 75% and 95% s 18.47, and bllon m 3 respectvely. Among them, the total annual runoff of 10 outflow water system areas s 18,099 bllon m 3. Table 1 shows the results of the annual runoff calculaton of the ncomng rver. The man rver water qualty analyss result s summary le table 2 shows. Table 1. Immgraton rver runoff calculaton Watershed Watershed area (m 2 ) Average annual runoff (Bllon m 3 ) Average annual runoff depth (mm) Dfferent frequency annual runoff (Bllon m 3 ) 40% 65% 85% Huang Fuchuan shmzugawa Gushanchuan Kuye rver Wudng Rver Total Through the analyss n Table 2, we can see that the chemcal type of dvng water n the area s relatvely smple, and the water chemstry types of desert beach and loess hlly regon are generally HCO 3 -CaMg type, Plans n some areas, due to strong concentraton, the water type s mostly CLSO 4 -N 8 -type water. 372

5 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 In order to test the effectveness of the model, the sample data set s dvded nto two parts: one s the tranng sample set, whch s used to buld the model; the other part s the test sample set, whch s used to verfy the model. The test sample does not partcpate n the process of model establshment. The montorng data of the mnng area s taen as the test sample set, and the model s valdated. The montorng data of the remanng statons are tranng sample data. Rver Table 2. Surface Water Qualty Analyss PH value Dssolved oxygen (mg/l) Oxygen consumpton (mg/l) Degree of mneralzaton (g/l) Luhe (Hengshan) Wudng Rver (Xangshu) Wudng Rver (Ba Jachuan) Tuwe Rver (upstream) Tuwehe (downstream) Kuyehe (upstream) Kuyehe (mddle) Kuyehe (downstream) Ja Lu Rver Eght Rver In order to process the redundant nformaton n the orgnal data and extract the more useful nformaton, the data s smoothed to elmnate the sngular value. Second, n order to elmnate the unt data of each ndex data dfferences, and the magntude of the dfference, to prevent the large number of "eat" decmal, the orgnal data normalzed. The nput and output varables of the sample are normalzed by the formula (8) to fall nto the (0, 1) nterval. p mn( p) new _ p, 0 n max( p) mn( p) Where p s a column vector of length n and represents one of the nput factors, t s possble to ncrease the number of hdden layers to complcate the networ and ncrease the tranng tme and the tendency of "overfttng". It wll reduce the result of neural networ; therefore, ths wor uses an nput layer, a hdden layer and an output layer of BP neural networ. In order to test the performance of GE_HSC neural networ, the tranng data of groundwater level n Yuln Cty s used to tran the networ and forecast. BP networ uses an ntermedate hdden layer, that s, nput - hdden layer - the output structure. (1) Tranng parameters: networ tranng usng tral and error method (tral and error) to determne networ parameters. The BP networ adopts structure, namely the nput layer 3 nodes, the mddle hdden layer 5 nodes, the output layer 1 node; hdden layer and output layer converson usng sgmod functon; the regularzaton of tranng nput sample parameters s n (0.2, 0.9), the number of neurons n the hdden layer s determned by tang a numercal range as the number of hdden layer neurons, accordng to the prevous results of the range of (3,11,15,21,31,33,38,44,52,59,65,70,73), the tranng error functon uses the tranng sample mean varance (V), value s ; the expermental platform uses Matlab, BP tranng functon tranlm; the maxmum number of tranng epochs = 500. Wrte GE_HSC algorthm code on Matlab; ntal populaton use random functon n (-5, 5) nterval generaton, populaton number 500; DE algorthm scalng factor F=0.3, crossover factor R=0.2; max evoluton algebra gen=600; ftness s ft = 1/V. Sample data a total of ten years of montorng records, of whch sx years of data tranng networ, the use of one hundred thousand data for testng. Global search through GE_HSC algorthm to acheve maxmum evoluton algebra, V= , the evoluton process shown n Fgure 3. (8) 373

6 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 As can be seen from Fgure 3, the representatve of the selected tranng samples s very mportant, f the test sample value exceeds the range of tranng samples, the error ncreases sgnfcantly. In the tranng, the sample values are larger or smaller samples reman n the predcton sample, observe the predcton results found that ths large or small sample error s sgnfcantly larger. In other words, tranng samples can cover the range of predcton samples; the overall accuracy and ndvdual accuracy are relatvely hgh. When the populaton s 200, the hdden nodes s 20, two groups of tranng, ndvdual maxmum relatve error reaches more than 9%; the number of populaton s 500, two groups n the tranng group, the maxmum error s 3.5%, whle the other group was 2.1%. 5. CONCLUSIONS In ths paper, the prncples and methods of soft computng methods such as fuzzy mathematcs, artfcal neural networ, genetc algorthm and DE algorthm are studed. Combnng the dfference evoluton algorthm (DE) wth the artfcal neural networ (ANN), the method of determnng the weghts of the artfcal neural networ s mproved. Based on the predcton of urban groundwater level and water qualty, the structure and nfluencng factors of GE_HSC networ are analyzed, the results show that there s no sgnfcant postve correlaton between the correlaton of the samples and the predcton accuracy, but the networ structure and the representaton of the samples have mportant nfluence. The results show that the correlaton between the tranng samples and the predcted samples has no sgnfcant postve correlaton. ACKNOWLEDGEMENTS Fgure 3. GE_HSC algorthm search process chart Ths wor s partally supported by the Fundng Project for Department of Yuln Unversty (16GK24), Natural Scence and Technology Project Plan n Yuln of Chna (2014cxy-11) Fundng Project for Department of Educaton of Shaanx Provnce of Chna (15JK1861), Natural Scence Basc Research Plan n Shaanx Provnce of Chna (2016KJXX-62).Thans for the help. REFERENCES Bagrov AM and Barton AF et al. (2013) An algorthm for mnmzaton of pumpng costs n water dstrbuton systems usng a novel approach to pump schedulng, Math Comput Model, 57(2), pp BIAN Zhengfu, INYANG Hlary I and DANIELS John L, et, al.(2010) Envronmental ssues from coal mnng and ther solutons, Mnng Scence and Technology, 20 (2), pp Booth C.J.(1986) Strata-movement concepts and the hydrogeologcal mpact of underground coal mnng, Groundwater, 24(4), pp Chao Bao, Chuang-ln Fang (2007) Water resources constrant force on urbanzaton n water defcent regons: A case study of the Hex Corrdor, ard area of NW Chna, Ecologcal Economcs, 62 (1), pp José Pnho, Ru Ferrera and Luís Vera et al.(2015) Comparson Between Two Hydrodynamc Models for Floodng Smulatons at Rver Lma Basn, Water Resour Manage, 29, pp

7 Rev. Téc. Ing. Unv. Zula. Vol. 39, Nº 10, , 2016 L, Ran (2015) Performance optmzaton for a class of generalzed stochastc Petr nets, Dscrete Event Dynamc Systems: Theory and Applcatons, 15(3), pp Quroga, VM and Popescu, I et al.(2013) Cloud and cluster computng n uncertanty analyss of ntegrated flood models, Journal of Hydronformatcs, 15(1), pp Rah, O.P.(2011) Optmzaton of hydro power plant desgn by Partcle Swarm Optmzaton (PSO), Proceda Engneerng, 30, pp Wen-Sheng Wang, Xue Hu-Feng, Zhang Feng (2014) A new dynamc regulaton UIPO model of groundwater based on cloud computng and hydronformatcs, Computer Modellng and New Technologes, 18(11), pp Xa L, Le Zhang and Xanje Cao et al. (2016) Retreval of precptable water vapor usng MFRSR and comparson wth other multsensors over the sem-ard area of northwest Chna, Atmospherc Research, , pp Yuan Xu, Hufeng Xue and Along Lu (2016) A Parallel Genetc Algorthm for the Verfyng Smulaton of the Performance Prototype, Rev. Téc. Ing. Unv. Zula, 39(7), pp Zhang Feng, Wang Wensheng, Zhang Yong-heng (2014) Study on the Harmonous Development Evaluaton Model between Groundwater Resources and the Envronment Economy -a Case of Yuln, Groundwater, 36(6), pp