An efficient approach for Weather forecasting using Support Vector Machines

Size: px

Start display at page:

Download "An efficient approach for Weather forecasting using Support Vector Machines"

Theodore Donald Arnold
6 years ago
Views:

1 0 Internatona Conference on Computer Technoogy an Scence (ICCTS 0) IPCSIT vo. 47 (0) (0) IACSIT Press Sngapore DOI: /IPCSIT.0.V47.39 An effcent approach for Weather forecastng usng Support Vector Machnes Tarun Rao N Raasekhar Dr T V Rankanth 3 Lea Eucaton an Research Infosys Lmte Assstant Professor VNRVJIETJNTU Unversty 3 Professor & Hea of the Department GRIETJNTU Unversty Abstract. Weather forecastng vta roe n the area of nvestgatng the weather parameters ke tme seres ata of ay maxma an mnma temperatures reatve humty hghest ran fa suen weather estructons abnorma weather changes have been forecastng or prectng usng many ata mnng technques as we as from the weather parameters tsef. Support Vector Machne (SVM) represents a nove approach neura network technque whch s use for cassfcaton regresson anayss an forecastng. In ths paper we sha be showng a nove approach for weather forecastng usng SVM. The experment stuy an outcome s compare wth Mut Layer Perceptron Learnng agorthm. Keywors: Back propagaton Weather forecastng Mut Layer Perceptron Support Vector Machne. Introucton Recenty weather forecastng/precton s very compex process an one of the very key tasks for researchers an acaemcans [] [] [3]. Now the weather forecastng s contnuousy ncreasng base on the tratona users such as agrcuture ar traffc servces an other sectors ke energy envronment that requre reabe nformaton on the present an future weather. In aton to ths the forecasters have to cope wth ncreasng ata voume notaby from Numerca Weather Precton moes; meteoroogca satetes raars an other observaton systems such as AWS wn profers an raometers etc. Forecasters focus on weather forecastng capabty nstea of osng tme accessng the nformaton Enhance an upgrae ther forecastng profcency wth many types of ata presentaton. Share ther profcency wth ther coeagues an transfer ther know-how to unor forecasters Browse vsuaze an anmate a the exstng ata ntutvey wth a mte number of actons Take avantage of mut-screens wth a GUI (Graphca User Interface) base on mut-wnows Use coaboratve toos for graphca proucton of expertse ata. Accurate forecast of weather parameters s a very ffcut task ue to ynamc nature of the atmosphere. Varous technques ke near regresson auto regresson Mut Layer Percepton(MLP) Raa Bass Functon networks are appe to cacuate approxmatey atmospherc parameters ke temperature wn spee ranfa meteoroogca pouton etc. [4][5] [6] [7] [8] [9]. Weather nformaton that aso ncues temperature wn spee sky cover humty etc. have aso been use n many of the short - term oa forecastng works [0]. But there s one compcaton: for m-term oa forecastng temperature nformaton for numerous weeks n avance s neee. If we wsh to use temperature nformaton n our moe we w aso want to forete the temperature. The usage of temperature therefore s not an ony opton when forecastng s performe for pero onger than one week. Yet the temperature forecastng s a much compex probem than oa forecastng. A neura network moe s a structure whch can be attune to create a mappng from a gven set of ata to features of or reatonshps between the ata. The moe s attune or trane usng a compaton of ata from a known source as nput usuay referre to as the tranng set. An Artfca Neura Network (ANN) 08

2 [] s an nformaton processng moe whch s strre by the metho boogca nervous systems such as the bran processes nformaton. A back propagaton network [] conssts of at east three ayers (mut ayer percepton): an nput ayer at east one ntermeary hen ayer an an output ayer.. BACKGROUND In recent years weather precton as rawn much attenton n many research communtes because t heps n safeguarng human fe an ther weath. A part from that t s usefu for enhancng natura caamtes agrcuture ye growth ar traffc contro marne navgaton forest growths an efense purposes... Support Vector Machnes(SVM): The Support Vector Machne (SVM) we known as kerne machne was eveope at AT & T Be aboratores by Vapnk an hs team [3] an t s a too that works base on the statstca earnng theory. The prncpa thought behn the Support Vector Machnes s that t tres to map the prma ata X nto a feature space terme as F wth a hgh mensonaty through a non-near mappng functon an thus bus the best possbe hyper pane n a nove space. An appcaton usng Support Vector Machnes (SVMs) for weather precton was presente n []. Tme seres ata of maxmum temperature at varous ocatons on a ay to ay bass s eberate so as to forecast the maxmum temperature of the subsequent ay at that ocaton epenng on the ay maxmum temperatures for a pero of preceng n ays referre to by the orer of the nput. Performance of the metho s anayze for a combnaton of spans of to 0 ays by makng use of the best possbe vaues of the kerne. Another supervse earnng metho that can be use for estmaton tasks s Support Vector Regresson (SVR). The SVR agorthm s an aton to the accepte cassfcaton too Support Vector Machnes (SVM). SVM s a machne earnng too whch has ts ancestry n statstca earnng theory [4]. In ths paper we evse the near support vector regresson to prect the maxmum weather at a ocaton. ( x y In orer to sove regresson probems we are gven tranng ata ) = 3... where x s a - mensona nput wth x R an the output s y R.The near regresson moe can be wrtten as shown beow [5]: f ( x) = ω x + b ω x R b R...() Where f (x) s a target functon an < > enotes the ot prouct n R So we measure the emprca rsk [5] we shou state a oss functon. Severa other aternatves are avaabe. The most common oss functon s the ε -nsenstve oss functon. The ε -nsenstve oss functon propose by Vapnk s efne the foowng functon: 0 L ε ( y ) = f ( x ) y ε... () the best possbe parameters an b n Eq.() are foun by sovng the prma optmzaton probem(l.p.wang[5]: + mn ω + C ( ξ + ξ )...(3) = + + y ω x b ε + ξ ω x + b y + wth constrants: ε ξ ξ 0 ξ =... where C s a pre-efne vaue whch etermnes the trae-off n between the fatness of f(x) an the amount + ξ up to whch evatons better than the precson are toerate. The sack varabes an ξ represent the evatons from the constrants of the ε -tube. Ths prma optmzaton probem can as we be reformuate as a ua one efne as foows: max ( )( ) ( ) ( )...(4) x x a a a a x x + y a a ε a + a = = = = 09

3 0 a a C wth constrants: =... an = ( a a ) = 0. Sovng the optmzaton probem efne by Eq.(4) an these constrants gves the best possbe Lagrange mutpers α an α whe ω an b are ω = (a a ) x b = ω( x r + x s ) gven by = x where r xs are the support vectors. Accorng to the compute vaue ofω the f(x) n Eq.() can be wrtten as: N f ( x) ( a a ) x x b...(6) = = + Hence s the precse formuaton of the cost functon an the use of the Lagrange theory. Ths souton has severa nterestng propertes. It can be proven that the souton thus foun s aways goba because the probem s convex. 3. MLPs are trane wth back propagaton agorthm Artfca Neura Networks (ANNs) are a parae as we as ynamc system of vasty nterconnecte nteractng parts base on the neuroboogca moes. There s cose anaogy between the structure of a boogca neuron an a processng eement of an ANN cae artfca neuron. Each neuron accepts nput sgna n the form of mathematca amount then generates the output sgna whch n turn actvates other neurons that recty connecte to t. As an outcome the nput sgna of a neuron s consere the summary sgna of a the outputs of neurons stanng before t n the network. The actvaton mechansm of each neuron epens on ts nner metho s cae actvaton functon an the sgnas communcates between neurons are represente n weghts. A mut-ayer network (aongwth one or more than one hen ayers) can earn any unnterrupte mappng to an arbtrary precson [6]. One hen ayer s consere aequate for ata earnng moe copng wth compcate ata factors. Fg. : Three-ayere fee forwar neura network. Agorthm: Step : Intaze the weghts that are present n the network (often ranomy) Step : Do Step 3: For every exampe e n the tranng set Step 4: O:= neura-net-output (network e) ; then forwar pass Step 5: T:= teacher output for e Step 6: Cacuate the error (T - O) at the sa output unts Step 7: Compute eta_wh for a the sa weghts from hen ayer to the output ayer;then backwar Pass Step 8: Compute eta_w for a the weghts from nput ayer to the hen ayer; backwar pass Contnue 0

4 Step 9: Upate a the weghts n the network Step 0: Unt a the exampes cassfe propery or stoppng crteron are satsfe Step : Return the sa network In the fg. the output of neuron n a ayer form whch moves the a the neuron. Therefore each neuron has ts very own nput weghts the weghts are fxe to one for each nput ayer.e. weghts are not change. The output s obtane by the appyng the nput vaues to the nput ayer whe passng the output of every neuron to the foowng ayer as nput. 4. Expermenta Anayss The rea wor atabases are hghy rty to nosy an mssng vaues. Therefore the ata can be normaze an preprocesse to mprove the quaty precton resuts of the ata. In ths work we coecte ata sets from Unversty of Cambrge for a span of 5 years (003-07) s use so as to bu the moes. In ths experment maxmum temperature of a ay s estmate base on the maxmum temperature of prevous n ays where n represents the best possbe ength of the span. The vaue of n s estabshe by the metho of expermentaton. The avaabe ata s agan sub-ve nto tranng vaaton an test sets. Tranng set s use so as to bu the moe vaaton set s use so as to perform parameter optmzaton an fnay test set s use so as to assess the moe. Separate moes are eveope usng SVM an MLP makng use of back propagaton agorthm. The performance of Mut Layer Perceptron(MLP) trane aong wth the back propagaton agorthm an SVM for verse orers n terms of Mean Square Error (MSE) s estabshe n Fg. From the erve outcomes t has been notce that the wnow sze w not have a key effect on the performance of MLP trane aong wth the back propagaton agorthm an SVM. Nonetheess t can aso be notce that rrespectve of the orer SVM performs superor to MLP. The Mean Square Error n the case of MLP vares from the vaues 8.0 to. epenng on the orer whst t s n the range of 7.06 to 7.68 n the case of SVM s. It can be observe that n the case of SVM there s no apparent varaton n the performance of the system ahea of the orer 5 an MLP performe fnest for orer 5.The error seems to essen to some extent for hgher orers n the case of SVM s but the tranng tme aso ncreases proportonatey aong wth the ncrease n orer. Thus orer 5 s seecte as best possbe wnow sze. 0 MSE grae SVM MLP Fg. Comparson n between MLP an SVM for verse graes 5. Concuson In ths paper we have effectvey propose the weather estmate base on support vector regresson moe an we compare both the SVM wth MLP for fferent egrees. Thus the outcome aso shows that SVM performs better than MLP trane wth back propagaton agorthm for a egrees. It was observe that

5 parameter choce n SVM s case has a noteworthy effect on the by an arge performance of the moe. As a resut we can concue that through proper seecton of the fferent parameters Support Vector Machnes can repace few of the neura network base moes for weather precton appcatons. 6. Acknowegements The author wou ke to thank a coeagues who contrbute to ths stuy. I am gratefu to Infosys Lmte for permttng me to come out wth ths paper. 7. References [] Y.Rahka an M.Shash Atmospherc Temperature Precton usng Support Vector Machnes Internatona Journa of Computer Theory an Engneerng Vo. No. Apr 009 [] Dens Roran an Barne K Hansen A fuzzy case-base system for weather precton. Engneerng Integent Systems Vo.0 No [3] Guhathakurtha P. Long-Range monsoon ranfa precton of 005 for the strcts an Sub-vson keraa wth artfca neura network. Current Scence Vo.90 No [4] Jae H.Mn. Young-chan Lee. Bankruptcy precton usng support vector machne wth optma choce of kerne functon parameters. Expert Systems wth Appcatons 8 pp [5] Mohanes M.A. Haawan T.O. Rehman S an Ahme Hussan A. Support vector machnes for wn spee precton. Renewabe Energy 9 pp [6] Pa N.R. Srmanta Pa Jyotrmoy Das an Kausk Maumar SOFM-MLP: A Hybr Neura Network for Atmospherc Temperature Precton. IEEE Transactons on Geoscence an Remote Sensng Vo.4 No pp [7] Pao-Shan Yu. Shen-sung Chen. I-Fan Chang. Support vector regresson for rea- tme foo stage forecastng. Journa of Hyroogy 38 pp [8] Stansaw Osowsk an Konra Garanty Forecastng of ay meteoroogca pouton usng waveets an support vector machne. Engneerng Appcatons of Artfca Integence 0 pp [9] We-Zhen Lu. Wen-Jan Wang. Potenta assessment of the support vector machne metho n forecastng ambent ar poutant trens. Chemosphere 59 pp [0] A. Jan an B. Satsh Custerng base short term oa forecastng usng support vector machnes n PowerTech Conference Bucharest Romana Juy 009. [] Mohsen Hayat an Zahra Moheb Appcaton of Artfca Neura Networks for Temperature Forecastng Wor Acaemy of Scence Engneerng an Technoogy [] Surat Chattopahyay Mutayere fee forwar Artfca Neura Network moe to prect the average summer-monsoon ranfa n Ina 006. [3] Haykn S. Neura Networks- A Comprehensve Founaton. Prentce Ha [4] Cortes C. an V. Vapnk (995). Support vector networks. Machne Learnng 0 (3) [5] L.P.Wang. (005). Support Vector Machnes: Theory an Appcaton Sprnger Bern 005. [6] Funamentas of Neura Networks archtectures agorthms an appcatons. Laurence Fausett Fora Insttute of Technoogy. Prentce-Ha Inc. 994.

International Journal "Information Theories & Applications" Vol.13

International Journal Information Theories & Applications Vol.13 290 Concuson Wthn the framework of the Bayesan earnng theory, we anayze a cassfer generazaton abty for the recognton on fnte set of events. It was shown that the obtane resuts can be appe for cassfcaton