Climate Change Prediction Using Artificial Neural Network

Transcription

1 Climate Chage Predictio Usig Artificial Neural Network Pooja Rajedra Choudhary School of Computig Sciece ad Egieerig, Vellore Istitute of Techology, Vellore, Tamil Nadu, Idia. Aachal Murade School of Computig Sciece ad Egieerig, Vellore Istitute of Techology Vellore, Tamil Nadu, Idia. Himai Thakur School of Computig Sciece ad Egieerig, Vellore Istitute of Techology Vellore, Tamil Nadu, Idia. Dr. M. Vekatesa School of Computig Sciece ad Egieerig, Vellore Istitute of Techology Vellore, Tamil Nadu, Idia. Abstract Climate forecastig is scietifically ad techologically challegig problem aroud the world which requires observig ad processig large amouts of climate data. Predictig the climate is importat for the best ad worst of climate. The aim of this paper is to predict raifall. I this paper we use classificatio techique, back propagatio artificial eural etwork for predictig raifall usig maximum ad miimum temperature. The performace of this algorithm is compared usig stadard performace metric such as mea square error ad correlatio coefficiet. A predictive artificial eural etwork model was also developed for the raifall predictio program ad the results compared with actual raifall data for the predicted periods. Keywords: Data miig; Raifall; Climate; Predictio; Back propagatio Artificial Neural Network. observatio. For keepig the track of chagig climate a metrological ceter prepares syoptic chart each ad every day ivolvig huge collectio of climate data obtaied from various climate statios. Numerical climate predictio It uses power of the computer for predictio of climate. Differet complex programs are ru o the supercomputer for providig differet iformatio of climate. But the equatios used i this method are ot precise. If the iitial stage is ot properly kow the climate predictio is ot accurate. Statistical climate predictio It is used alog with the umerical methods. I this method it uses past records of climate data for predictig the future climate. The oe of the purpose is to fid various aspects of climate which are good idicators of future climate. Itroductio Climate predictio for the future is most importat attribute to forecast because most of the idustries as well as agriculture sectors are largely depedet o the climate coditios. Sice the aciet times climate predictio is oe of the fasciatig ad iterestig domais. It is used to predict ad war about various atural disasters which are caused due to chages i climate coditios. Due to cofusig ature of atmosphere, we require more computatioal power to predict the atmosphere for solvig its complex equatios. Climate forecastig may be less accurate, because of the differece betwee the past time ad the future is more. With the help of these models we ca miimize this error for predictig the most correct outcome. The steps ivolved i predictig the climate are as follows: 1. Data collectio such as maximum ad miimum temperature 2. Data assimilatio. 3. Data aalysis 4. Numerical climate predictio The various methods used i predictio of climate are: Syoptic climate predictio It is oe of the approaches for climate predictio. It refers to the differet climate elemets withi the specific time of the Related Work Ivestigatio was carried out for the problem of esemble learig i raster classificatio. This problem is importat i may applicatios such as classificatio i medical image processig, lad cover classificatio i remote sesig. The problem is challegig due to the effect of class ambiguity from spatial heterogeeity. A ovel spatial esemble the framework, which is further used for partitioig a raster framework ito differet spatial footprits for miimizig class ambiguity of traiig, samples (Zhe Jiag et al. 2014). Artificial eural etwork approach ca be used to solve oliear problems of regressio arises i evirometal modelig which icludes forecastig for a short term period which also icludes raifall ruoff modelig ad atmospheric cocetratios which leads to the pollutio. The aim was to review a existig methodology for estimatig predictive ucertaity as evirometal datasets are redudat ad oisy which describes how a predictive distributio model ca be used to assess the impacts occurred due to the climate chage ad to improve the differet decisio to avoid the differet impacts of it(cawley G.C et al. 2006). Ivestigatio was carried out to fid relatio betwee differet metrological factors ad Newcastle disease ad the diffreet key factors that access the Newcastle diseases has bee determied. For this Newcastle disease forecastig model was 1954

2 bee built ad apply Back Propagatio eural etwork classificatio techique has bee used i aimal disease forecastig for their research (Hogbi Wag et al. 2009).For discovery covariate which is based o a Poisso descriptio with high frequecy Bayesia framework ad Sparse regressio model of hierarchical Bayesia was used to idetify the co-variates which affects the precipitatio frequecy which are collected from the various observatios at differet statios over may differet climatologically regios which are i the US cotietal(debasish Das et al. 2014).The For combiatio ad represetatio Dempster- Shafer theory of evidece was used for ucertaity which has bee see i differet sources. Bayesia ad Dempster- Shafer approaches for ucertaity modelig are used for the predictio hydrologic drought which comes uder climate chage(deepashree Raje ad P.P.Mujumdar 2010).A dyamic etworks-based methodology Spatio-Temporal Physical System is used i advace future predictio for emergig extreme evets of dyamic tracks such as hurricaes or forest fires (Huseyi Seca et al. 2012). Usig data such precipitatio, temperature, pressure the study was carried out for modelig mothly speed values, which also icludes how this factors are importat for the estimatio of these differet variables. The parameters such as pressure ad precipitatio have the approximatio values of 40% ad 10% respectively (M. A. Ghorbai et al. 2013). A compariso was made betwee the performaces through predictio evapotraspiratio by the pa evaporatio method ad the FAO56 Pema-Moteith method usig the artificial eural etwork. The relatioship betwee evaporatio meteorological factors is determied, a artificial eural etwork was used (Seema Chauha R. K. Shrivastava 2008).The data miig techique (artificial eural etwork) was used to predict the lo future raifall of the local-scale from three differet coarse-scale GCMs. The case study has bee selected urba draiage catchmet for the area of orthwester Eglad. For uderstadig ad quatifyig the potetial hazard from surface floodig, the fial assessmet was doe with the help of risk assessmet ad calculated local raifall methodology (M. Abdellatif et al. 2015). This paper work is based o solar forecastig of solar radiatio which affects the productio of photovoltaic. The variatio i the climate ad is features which makes it difficult to predict solar radiatio. The quatity of solar radiatio of depeds o latitude of the differet geographical locatios ad its differet characteristics.the Extreme Learig Machie has bee used for traiig a eural etwork model which is used for the predictig the solar illumiace. This traied eural etwork has bee challeged o solar illumiace of two year groud (Ferrari,S. et al. 2012).Data miig approach for raifall predictio was used. The Bayesia classificatio techique was used ad results i a good predictio performace, ad ca be used for class predictio problems. The Bayesia predictio model ca easily lear ew classes. The accuracy icreases with the icrease of learig data (Nikam, V.B ad Meshram, B.B 2013).The Neural Network has differet types of algorithm oe of which is back propagatio eural etwork. This techique was used for predictig the temperature. The mai advatage of the BPN eural etwork method is that it ca be used to fairly To approximate a large values class of fuctios the back propagatio eural etwork method ca be used for effective predictio. This method is better tha umerical differetiatio (I.Kadar Shereef, Dr. S. Sathosh, 2011). The Empirical method ad Dyamical method are maily two approaches for predictig raifall. The approach here is empirical approach which is based o aalysis of historical data. Oe of the most used approaches is empirical approach for climate predictios are artificial eural etwork, regressio, fuzzy logic. This paper use data miig techique which icludes classificatio ad clusterig techiques for raifall predictio. This paper applied eural etwork for raifall predictio ad the eural etwork Bayesia regularizatio has bee applied i the implemetatio (Jyothis Joseph, Ratheesh T K 2013). I the world which cotai o homogeous datasets the framework for learig robust predictive models does ot cotais the eough umber of traiig samples were preseted. Remote sesig for forest cover estimates has bee demostrated by usig this framework (Auj Karpate 2015).I this paper they have used evaluatio of feed forward back propagatio eural etwork. This back propagatio model has bee compared to other covetioal methods. A ew approach has bee adopted by usig few variables. Results show that BP performs better tha BCR ad RMBF methods (S Traore 2015). Methodology Artificial Neural Network A eural etwork is the tool that is used for data modelig ad is used to fid out the relatioship betwee iputs ad outputs which may be complex. The idea of eural etwork techology is ispired by huma biological huma ervous system which icludes huma brai which processes the iformatio. It is used to perform itelliget tasks which are as similar to the task which is performed by huma brai. Neural Network almost performs similar task same as performed by huma brai. The task which is similar is as give below: Like huma brai etwork gais kowledge by learig ad traiig. It uses iter-euro coectio stregths to store kowledge which is called syaptic weights Neural Network is a structure or a etwork of itercoected uits of large umber of euros. The local computatio has bee carried out based o the characteristics of iput ad output euros which implemets the fuctio for this calculatio. The fuctio (weighted sums of iputs) which produces a output if it exceeds a give threshold. The output ca use as a iput to other euros i the etwork. This process repeated util a fial output is produced. Neural etwork are used for represetig the relatioships betwee liear ad o-liear from the data directly which has beig modeled. A eural etwork model is a etwork of euros. It is used to map the relatioship betwee a give set of data or is used amog the data. The data model collects the data from differet sources as iput. This iput data is kow as traiig set. After traiig the iput data, the classificatio, 1955

3 predictio, ad simulatio are perform by eural etwork o ew data from similar sources. Figure 1: Neural Network block diagram Differet types of eural etworks are as follows 1. Feed forward Neural Network 2. Hopfield Neural Network. 3. Radial Basis Fuctio (RBF) Neural Network 4. Recurret Neural Networks Back Propagatio Neural Network Our method for predictig the raifall is Back propagatio eural etwork. The etwork cosists of at least three layers (multi layer perceptio): the iput layer, at least oe hidde layer which is also kow as itermediate hidde layer ad the output layer. I this model uits are coected i feed forward fashio, iput uits are coected with uits of hidde layer ad hidde layer uits are coected with the uits of output layer. The iput patter is propagated i feed forward directio towards the output through the iput layerto-hidde layer ad hidde layer-to-output layer weights whe a back propagatio etwork is to be cycled. The ame of algorithm itself suggest that the errors propagate backward directio from output layer odes to odes of hidde layer ad the from hidde layer odes to the odes of iput layer. Back propagatio algorithm is used to determie gradiet of the error of the eural etwork with respect to the modified weights of the etworks. To miimizig the errors this calculated gradiet is used which will be helpful that predictio is earby to actual output. The proposed method for predictig Raifall by usig Back propagatio Neural Network is tested by usig huge dataset. The predicted values of raifall are compared with the actual raifall data of the particular year regio wise. So this will be helpful to the meteorologist to predict the future weather easily ad accurately. Back Propagatio Approach Phases i Back propagatio Techique: There are two mai steps ivolved i back propagatio algorithm (Baboo S.S. ad Shereef I.K) which is give as follows: 1. Propagatio of weights 2. Updatig of weights Step1: Propagatio of weights The propagatio i eural etwork ivolves differet steps which are give as follows 1. Traiig patter's iput is propagated i forward directio i the eural etwork is used to geeratio the output activatio 2. This activatio outputs are back propagated ad traiig patters of the targets are used to calculate the deltas for all outputs odes ad hidde odes of the artificial eural etwork Step2: Updatig of weights 1. The output delta which has bee calculated is multiplied with iput activatio for the calculatio of gradiet for that particular weight 2. After addig the ratio with the weight it is brought i the directio of gradiet 3. As it impacts the learig of artificial eural etwork, this ratio is essetial. This learig of artificial eural etwork is called learig rate. To fid out where the error is icreasig the sig of gradiet is essetial which the reaso to update weights i opposite directio is. These steps are repeated util we get the correct value for the predicted raifall. i. Learig modes i artificial eural etwork There are two modes used for learig i a artificial eural etwork oe is batch learig ad other is olie learig modes. I batch learig mode of artificial eural etwork propagatio is doe before weight updatig ad i olie learig mode of artificial eural etwork propagatio is doe after the weight updatig. ii. Algorithm for Backward Propagatio The algorithm for a three-layer etwork model (that is for oe hidde layer) is give as: Step 1: All the iputs Weights i artificial eural etwork are iitialized ad Step 2: Do till Step 3: I the traiig set for each e O = deotes predicted output of eural etwork (etwork, e); // forward pass T =deotes actual output of eural etwork for e Step 4: The the error is calculated betwee actual output ad eural etwork predicted output at the output odes Step 5: Calculate the delta values for hidde layer through the weights from hidde layer odes to output layer odes //propagatio i backward directio Step 6: Calculate the delta values for hidde layer through the weights from iput layer odes to the hidde layer odes; // propagatio i backward directio Step 7: After calculatio of weights, updatig are doe i etwork Step 8: Do till criterio satisfies Step 9: Retur the etwork 1956

4 layer. The after doig this determie error i the output layer ad its previous layer. The give below equatio is a back propagatio equatio which helps to calculate the partial derivative of the E p error w.r.t y i i.e. (the activatio value) up to the th layer. Now calculate the partial derivative of the error w.r.t output of the last layer euros. The formula for error calculatio is as follow: E p = ½ (x i T i ) 2 (1) Takig the partial derivative of give equatio (1) E p x i = x i - T i (2) Figure 2: Back propagatio Neural Network block diagram Approach for Raifall Predictio 1. I every layer the output of each euro is moved to every other euros i that layer 2. Every euro has its iput weight. 3. I iput layer, iput values are fixed i.e. assumig the weights for each iput bias i the iput fixed. 4. The output is calculated usig iputs values at the iput layer odes ad the output of this euros acts as a iput to the other iput layer odes which are hidde layer odes ad output layer odes values. 5. The Back Propagatio Neural Network may cotai ay umber of hidde layer but it is compulsory to have oe iput ad oe output layer I this Network the umber of euros is equal to the umber of iputs provided at the iput layer. The umber of euros preset at the output side is decided by the umber of output eeded by the user. I this etwork, total umber of hidde layers ad euro i the hidde layer are ot fixed i.e. they ca vary accordig to data ad cofiguratio of etwork. The performace of the etwork decreases whe hidde layers are added to the etwork but the same hidde layers are used to evaluate complex patters. The etwork cofiguratio has a sigle hidde layer, but if the eural etwork model is ot learig well the the other hidde layers ca be added to the etwork accordigly. The iputs to the eural etwork are as follows Raifall Maximum Temperature Miimum Temperature Back propagatio is a iterative process that moves i backwards directio from output layer to iput layer through hidde layer util the first layer of etwork is reached. Suppose the error at the output layer is kow the it is easy to determie the chages i the weights of iput euros, to miimize the error. The error of the previous layer output ca be calculated usig back propagatio, by takig the output of curret layer as the feedback value. Now apply this iteratio: start from the output layer ad the calculate the chages of weights of this output This iitial value of back propagatio eural etwork is calculated by usig equatio (2). If the values preset at the right side of the equatio (2) i umeric value the the derivative is also umeric. These umeric values of the derivative are used to calculate the chage which has occurred i the weights by usig equatio (3) ad (4): E p y i = G (x i ) Ep x i Here G (x i ) deotes the derivative of the fuctio E p w ij =x j E p 1 y i The, agai usig equatios (2) ad (3) for calculatig the previous layer odes errors, ow usig the below equatio (5): E p 1 y 1 i = iw ik Ep y i The startig values for immediate previous layer are calculated usig the values obtaied from equatios (5).For the previous layer the umeric values that is calculated from equatio (5) is used i equatio (3),(4) ad (5). The equatio (4) represet the chage i weight occurred i the curret layer. ad the weight is updated by usig the give below formula: (3) (4) (5) (w ij )ew = (w ij )old- eta.( Ep ) (6) w ij Here eta shows the rate of learig. Results ad Discussio To evaluate the proposed eural etwork model the dataset is take from Idia Istitute of Tropical Meteorology [16].This dataset cotais miimum, maximum temperature ad actual raifall data particular period of time. The data which we have used for predictio is of North-East Idia ad the the aalysis was doe usig eural etwork model for the year 2015 ad the the predicted values were compared with actual values of raifall (i Degree Cetigrade). The graph for the year 2015 is as give below: 1957

5 The results are compared with previous predicted values of raifall of Cheai regio for the year Here the mea of maximum temperature, miimum temperature ad raifall were give ad the predicted values of raifall are compared with actual value of raifall (i Cetigrade).The above graph is for Cheai regio for raifall predictio. Coclusio I this study, it ca be cocluded that a feed-forward artificial eural etwork model usig back-propagatio algorithm is developed to predict the raifall usig miimum ad maximum temperature. The outputs show that a appropriate accuracy ca be achieved by this method. The artificial back propagatio approach for climate chage predictio is calculate the efficiet ad accurate results of raifall predictio ad ca be used as a alterative method for predictio of other climate chages. However i future it ca be exteded to predict climate chage with other parameters for large regio. Refereces [1] Zhe Jiag; Shekhar, S.; Kamzi, A.; Kight, J., "Learig a Spatial Esemble of Classifiers for Raster Classificatio: A Summary of Results," i Data Miig Workshop (ICDMW), 2014 IEEE Iteratioal Coferece o, vol., o., pp.15-18, Dec [2] Cawley, G.C.; Haylock, M.R.; Dorlig, S.R., "Predictive Ucertaity i Evirometal Modellig," i Neural Networks, 2006, IJCNN '06. Iteratioal Joit Coferece o, vol., o., pp , 0-0 0, July 16-21, [3] Hogbi Wag; Duqiag Gog; Jiahua Xiao; Ru Zhag; Li Li, Oe Predictio Model Based o BP Neural Network For Newcastle Disease i Computer ad Computig Techologies i Agriculture II, Volume 2, the Secod IFIP Iteratioal Coferece o Computer ad Computig Techologies i Agriculture (CCTA2008), October 18-20, [4] Das,D.; Gaguly; A.R.; Obradovic, Z., A Bayesia Sparse Geeralized Liear Model with a Applicatio to Multiscale Covariate Discovery for observed Raifall Extremes over the Uited States, i Geosciece ad Remote Sesig, IEEE Trasactios o, vol.53, o.12, pp , Dec doi: /TGRS [5] Deepashree Raje; P.P. Mujumdar, Hydrologic drought predictio uder climate chage: Ucertaity modelig with Dempster Shafer ad Bayesia approaches, i water Resources 33 (2010) [6] Huseyi Seca; Zhegzhag Che; William Hedrix; Tatdow Pasombut; Frederic Semazzi; Alok Choudhary; Vipi Kumar; Aatoli V. Melechko; ad Nagiza F. Samatova, Classificatio of Emergig Extreme Evet Tracks i Multi variate Spatio -Temporal Physical Systems Usig Dyamic Network Structures: Applicatio to Hurricae Track Predictio, i Twety-Secod Iteratioal Joit Coferece o Artificial Itelligece NSF CCF (2012). [7] M. A. Ghorbai; R. Khatibi; B. Hosseii; M. Bilgili Relative importace of parameters affectig wid speed predictio usig artificial eural etworks i theoretical ad applied climatology, October 2013,Volume 114,issue 1,pp [8] Seema Chauha; R. K. Shrivastava Performace Evaluatio of Referece Evapotraspiratio Estimatio Usig Climate Based Methods ad Artificial Neural Networks i water resources maagemet, March 2009, volume 23, issue 5, pp [9] M. Abdellatif1; W. Atherto1; R. Alkhaddar1, Y. Osma2 Flood risk assessmet for urba water system i a chagig climate usig artificial eural etwork i atural hazards, July 2015, pp [10] Ferrari, S.; Lazzaroi, M.; Piuri, V.; Salma, A.; Cristaldi, L.; Rossi, M.; Poli, T., "Illumiace predictio through Extreme Learig Machies," i Evirometal Eergy ad Structural Moitorig Systems (EESMS), 2012 IEEE Workshop o, vol., o., pp , Sept [11] Nikam, V.B.; Meshram, B.B., "Modelig Raifall Predictio Usig Data Miig Method: A Bayesia Approach," i Computatioal Itelligece, Modellig ad Simulatio (CIMSim), 2013 Fifth Iteratioal Coferece o, vol., o., pp , Sept

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