Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction
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1 Spatio-Tempora Asynchronous Co-Occurrence Pattern for Big Cimate Data towards Long-Lead Food Prediction Chung-Hsien Yu, Dong Luo, Wei Ding, Joseph Cohen, David Sma and Shafiqu Isam Department of Computer Science University of Massachusetts Boston, Boston, MA Emai: {csyu, duo, ding, Department of Civi and Environmenta Engineering Tufts University, Medford, MA Emai: {David.Sma, Abstract Recent research efforts aim at utiizing Big Cimate Data to predict foods 5 to 15 days in advance. Current simuation modes forecasting heavy precipitation, a major factor reated with food occurrences, are computationay expensive and imited by their error ampification. In this paper, we introduce Spatio-Tempora Asynchronous Co-Occurrence Pattern to associate heavy precipitation with dense precipitabe water and expore ong-ead food prediction from machine earning perspective. Our mode predicts one ocation s fooding risk by connecting the heavy precipitation with its preceding precipitabe water through a association mining method for asynchronous co-occurrence ocation discovery and a spatiotempora ensembe earning method for predictive modeing. Our framework requires ess computationa cost and smaer train data whie being compared to other existing approaches. In addition, our framework is designed to be scaabe and aows distributed computing. Our rea-word case study has achieved 87% accuracy on predicting the heavy precipitations which trigger severe foods at east 9 days in advance. Keywords-Spatiotempora Patterns; predictive modeing; Food Prediction I. INTRODUCTION Atmospheric and cimate data have been intensivey coected through various means and keep increasing enormousy over time. Simuating atmospheric circuation using ensembe regression modes is the most common way of extreme weather forecasting [1]. However, this type of simuations are computationay expensive when processing the goba atmospheric data to simutaneousy buid the modes [2]. Besides, the simuation errors are ampified after running regression over a ong ead-time [3]. Therefore, scientists are sti strugging to utiize this unstructured Big Cimate Data [4] to predict severe foods with acceptabe accuracy 5 to 15 days ahead. The extreme precipitation accumuation has been studied by [5], [6] and is assumed to be a major trigger of foods. Since heavy precipitation come from dense precipitabe water retained in the atmosphere [7], we propose a new concept, Spatio-Tempora Asynchronous Co-Occurrence Pattern (STACOP), to extend the causa factors of foods from oca precipitation to goba precipitabe water. Based PWC B PWC A Target Location PWC Figure 1. A Spatio-Tempora Asynchronous Co-Occurrence Pattern is an association connecting goba dense precipitabe water (PWC) to a target ocation s formation of heavy precipitation (EPC). This formation progresses over space and time and takes a transformation time of ength. The bars represent the time windows within the same periods. on this concept, we design a tempora association mining method to identify the asynchronous co-occurrence between one ocation s heavy precipitation and dense precipitabe water of other ocations. To the best of our knowedge, we are the first team to propose a spatio-tempora modeing framework connecting food, heavy precipitation, and dense precipitabe water for ong-ead food prediction. We first define Extreme Precipitation Custer (EPC) as a time window to represent the heavy precipitation accumuated within this window at a ocation. To ensure the EPCs identified by our search agorithm having the highest potentia of triggering foods at one ocation, we utiize this ocation s historica severe fooding events to approximate this ocation s water-hoding capacity [8] with three threshods. We further define Precipitabe Water Custer (PWC) as a time window to represent dense precipitabe water measured within this window at a ocation. Our key assumption is that any EPC is contributed by the occurring at certain cooccurrence ocations some time earier [5]. Using Figure 1 as an exampe, if there are at ocation A, B, and C today C EPC
2 Spatio-Tempora Asynchronous Co-Occurrence Pattern Loca Precipitation Data Figure 2. EPCs Severe Foods Tempora Association Mining Loca Food Records Goba Precipitabe Water Data Big Data of Cimate Severe Food Prediction Predictive Modeing Feature Extraction Asynchronous Co-occurrence Locations Goba Atmospheric Data The proposed framework for ong-ead food prediction. and these are moving toward the target ocation under certain circumstances, one week ater, heavy rainfa starts to drop at this target ocation for days and to form an EPC due to the approaches of these. This scenario is considered as an STACOP of this target ocation s food occurrences. A, B, and C are caed the Asynchronous Co-occurrence Locations of this STACOP. The asynchronous co-occurrence ocations are identified by our tempora association mining method which extracts the most associated spatia features before the predictive modeing. Therefore, we reduce the training data size and remove the irreevant and redundant factors from the entire spatio-tempora domain. We further adopt a tempora cascading voting approach to assembe the Decision Tree modes earned from different ead-time and buid the fina predictive mode. An outook of our framework is shown in Figure 2. We have conducted a case study to predict Iowa s sever fooding events using thirty years of historica atmospheric and daiy precipitation data of Iowa. Our association mining method identifies the asynchronous co-occurrence ocations associated with the EPC occurrences in Iowa. The resuts obtained from the modes using the features extracted from asynchronous co-occurrence ocations have the advantages on computationa cost and prediction performance over the modes buit on the entire spatia space. Overa, our contributions are: We are the first to utiize the historica severe fooding events to approximate the water-hoding capacity for identifying the EPCs with the highest potentia of triggering foods. We introduce Spatio-Tempora Asynchronous Co- Occurrence Pattern to associate heavy precipitation to dense precipitabe water and then deveop a tempora association mining method to identify the spatia associations between one target ocation and its asynchronous co-occurrence ocations. Using features extracted from these asynchronous co-occurrence ocations, we dramaticay reduce the training data size for modeing. Our framework fits the Map-Reduce-stye paraeization and is scaabe. Our rea-word case study has shown that our ensembe mode requires ess computationa cost and has achieved 87% accuracy whie predicting severe foods at east 9 days in advance. The rest of this paper is organized as foows. In Section II-A, we define EPC and discuss how to approximate the water-hoding capacity. Next, we define PCW and introduce STACOP to associate EPCs and with a tempora association mining method to identify the asynchronous co-occurrence ocations in Section II-B. We then propose a spatio-tempora predictive mode for food prediction in Section II-C. In Section III, we evauate our proposed framework through a case study using the rea-word data sets. The reated works are then discussed in Section IV. Finay, our study is concuded in Section V. II. THE PROPOSED FRAMEWORK To connect extreme precipitation and dense precipitabe water for ong-ead prediction on the food triggered by heavy rainfas, our framework incudes three major tasks, heavy precipitation discovery, asynchronous co-occurrence ocation identification, and predictive modeing. A. Extreme Precipitation Custer Discovery Fooding is often produced by the abnorma increase in precipitation within certain period of time. We consider the heavy precipitation within a time window as an Extreme Precipitation Custer. Definition 1: An Extreme Precipitation Custer (EPC) is a time series data consisting of n precipitation measurements, p 1, p 2,..., p n, coected at a certain n ocation, where i=1 n π, p i θ p : i = 1, 2,..., n, and p i n > α p. By our design, an EPC can be used to represent the true nature of a tempora bocking of different time window engths. The α p and θ p can be referred to as the voume threshods and π can be referred to as the accumuation speed threshod. Different ocations have different waterhoding capacities for precipitations [8]. In other words, when the precipitation eve exceed one ocation s waterhoding capacity, food is very ikey to occur at this ocation. Therefore, the accumuation speed and voume of precipitation are two main indicators of food occurrences. We et α and θ be the percentie between 0% to 100% and α p be the α th percentie vaue and θ p be the θ th percentie vaue among a coection of precipitation measurements. To approximate one ocation s water-hoding capacity, we propose using this ocation s historica severe fooding events to vaidate α, θ and π. If a severe food is covered by an EPC, this food s starting time is within or soon after the time window of this EPC. We then define Positive Extreme Precipitation Custer (P-EPC) as an EPC which covers a severe fooding event. From each setting of α, θ, and π, we can identify a set of EPCs incuding a subset of P-EPCs. We exam a the settings and find those settings with which
3 PWC Lead Time=7 3 Co-Occurrence Band=3 EPC ength(pwc)=8+3= Co-Occurrence Rate=3/11 Figure 3. Co-Occurrence Band and Co-Occurrence Rate of a ocation are cacuated based on the overap between one time-shifted PWC at this ocation and one EPC at the target ocation. our search agorithm identifies a set of P-EPCs covering every historica severe fooding. We choose the setting with the highest P-EPC rate, the tota number of P-EPCs divided by the tota number of EPCs, as the best configuration to identify the EPCs with high possibiity of causing foods. B. Spatio-Tempora Asynchronous Co-Occurrence Pattern Heavy precipitation comes from dense precipitabe water [7]. The precipitabe water is the tota water vapor contains in an atmospheric coumn bottomed at the ground surface and wi start to turn into precipitation under certain conditions, such as the changes of temperature [9]. Simiar to EPC, we define Precipitabe Water Custer (PWC) to represent dense precipitabe water. Definition 2: A Precipitabe Water Custer (PWC) is a time series data consisting of n precipitabe water measurements, w 1, w 2,..., w n, at a certain n ocation, where n π, i=1 w i θ w : i = 1, 2,..., n, and w i n > α w. Considering the transformation from precipitabe water to precipitation progressing over space and time (Figure 1), we define Spatio-Tempora Asynchronous Co-Occurrence Pattern (STACOP) as foows. Definition 3: A Spatio-Tempora Asynchronous Co- Occurrence Pattern (STACOP) of a ocation G is a transformation pattern indicating the fact that the occurring at the asynchronous co-occurrence ocations on time t aways ead to the occurrences of EPCs on time t + at G under certain circumstances, where is the ead-time of this STACOP. To identify asynchronous co-occurrence ocations, we propose a tempora association mining method with two new measures, Co-Occurrence Band (COB) and Co-Occurrence Rate (COR), used to evauate the reationship between a target ocation s EPCs and a source ocation s PWC. We first define an overap() function to cacuate the tempora association between two custers. Definition 4: Given an EPC P = {t a+1, t a+2,..., t a+q } and a PWC W = {t b+1, t b+2,..., t b+r }, where a and b are the start time, and q and r are the engths of P and W, respectivey. Given a ead-time, we have Ṕ = {t a+1, t a+2,..., t a+q }, where Ṕ is obtained by shifting P by forward. An overap function, denoted as overap(p, W, ), returns ength(ṕ W ) as the tempora association between P and W. This overap() function returns the overapping ength of a PWC and a time-shifted EPC and is used to indicate the possibe contribution of this PWC to this EPC over time. Using Figure 3 as an exampe, after shifting the PWC by 7 forward, the overapping between the EPC and the shifted PWC is 3. We consider this overapping indicates the degree of contribution by the PWC to the EPC. Next, we define Co- Occurrence Band to measure the association of a source ocation with respect to a target ocation. Definition 5: At a target ocation G, there are tota j EPCs, P 1, P 2,..., P j. Meanwhie, at a source ocation S, there are tota k, W 1, W 2,..., W k. The Co- Occurrence Band (COB) of ocation S with respect to ocation G with a ead-time is cacuated as: COB(S, G, ) = j k x=1 y=1 overap(p x, W y, ). We consider the source ocations having high COB with respect to a target ocation have high possibiities of being the asynchronous co-occurrence ocations of this target ocation. However, certain ocations having high COB is due to the ong ength of, not due to the transformation from to EPCs. Therefore, the ocations with ong consistenty have dense precipitabe water so the target ocation s EPCs aways overap with these ocations. We further introduce Co-Occurrence Rate to measure this situation. Definition 6: The Co-Occurrence Rate (COR) of ocation S with k, W 1, W 2,..., W k, with respect to ocation G with a ead-time is cacuated as: COR(S, G, ) = COB(S,G,). k ength(w y) y=1 A iustrative exampe of how to cacuate COB and COR is given in Figure 3. A ocation with ow COR means that the occurring at this ocation does not frequenty contribute to the formations of the EPCs at a target ocation after a ead-time, so this ocation is not considered as asynchronous co-occurrence ocation. Distributed computing can be appied to this asynchronous co-occurrence ocation discovery since the evauation of each ocation is independent from each other. C. Spatia-Tempora Predictive Modeing With different ead-time, we obtain different sets of asynchronous co-occurrence ocations to indicate the progression of EPCs over spatia space. Using the atmospheric factors coected at each set of asynchronous co-occurrence ocations as the features, we train the modes under different given ead-time. These modes earn the conditions under which the occurring at the asynchronous co-occurrence ocations are most ikey to form P-EPCs which eventuay trigger foods at the target ocation. Each mode can be trained via distributed computing because
4 Tabe I THE SETTINGS WITH THE HIGHEST P-EPC UNDER DIFFERENT π. α θ π P-EPCs EPCs Rate 87.8% 39.5% % 87.8% 39.5% % 84.7% 46.2% % 78.3% 46.7% % the data used to train an individua mode is independent from the others. We choose the Decision Tree agorithm to buid these modes because of its buid-in feature seection and interpretabe output. To consoidate these modes into one ensembe predictive mode, we propose a Tempora Cascading Voting Ensembe Modeing approach with the considerations of a contributive factors over spatia and tempora domains. If the majority of the Decision Tree modes predict the same P-EPC occurrence at the target ocation, our ensembe mode outputs a positive prediction of a P-EPC occurrence. A P-EPC prediction on time t means that there wi be a severe food occurring on or after time t + π, where π is the minima ength of an P-EPC. As a resut, we can predict the severe foods caused by heavy precipitation at east +π in advance, where is the shortest ead-time used in the mode earning process. III. CASE STUDY: LONG-LEAD FLOOD PREDICTION IN A. Data Sources Description IOWA The daiy averages of the precipitation accumuations measured in Iowa between 1980 and 2010 are used to investigate the EPC concurrences. We aso obtain Iowa s severe fooding events occurring between 1980 and 2010 from the Iowa Homeand Security and Emergency Management s website [10]. Next, we eveny divide, by 2.5 degree atitude-wise and 2.5 degree ongitudes-wise, the northern hemisphere into 5, 328 geographic ocations and then extract historica atmospheric data recorded at each ocation from the NCEP-NCAR Reanaysis dataset [11]. We seect nine atmospheric factors, 1,000, 500, and 300 hectopasca (hpa) geopotentia height, the temperature at 850 hpa geopotentia height, the speed of zona wind at 850 and 300 hpa geopotentia height, the speed of meridiona wind at 850 and 300 hpa geopotentia height, and the tota precipitabe water in the coumn of the atmosphere, as the features of each ocation. B. Identifying Extreme Precipitation Custers Three threshods, θ, α, and π, are needed for EPC discovery. By varying θ and α from 0% to 100% and π from 3 to 14 days, we identify the EPCs in Iowa between 1980 and Using the records of the historica severe fooding events [10], we evauate each configuration by comparing the dates of fooding events with the dates of the EPCs identified. If there is a fooding event occurring during or right after an EPC, this EPC is marked as a P-EPC and this fooding event is considered being covered by this P- EPC. For each given π, we seect the maxima θ and the maxima α with which our searching agorithm finds the EPCs covering every fooding events as the best setting. We then find the best setting of θ and α under different π, which are isted in Tabe I. When π 7, not a the fooding events are covered by the identified EPCs. The setting of θ = 46.2%, α = 84.7%, and π = 5 days is seected as the best configuration because it yieds the highest P-EPC rate and is then used in the rest of our experiments. C. Identifying Precipitabe Water Custers and Asynchronous Co-Occurrence Locations Using the daiy precipitabe water of those 5, 328 ocations, identify the of every ocation with the same setting of θ = 46.2%, α = 84.7%, and π = 5 days. However, we obtain percentie vaues from three different spatia scaes of precipitabe water data. The goba percentie vaues are obtained from the entire precipitabe water data because they represent the ranking of entire precipitabe water of the northern hemisphere. The oca percentie vaues are obtained from the precipitabe water data of every individua ocation. As a resut, each ocation has its own oca percentie vaues. The third type of percentie vaues are caed target percentie vaues. In our case, these vaues are obtained from the precipitabe water data observed at those ocations in Iowa. Using these three types of percentie vaues obtained by different spatia scaes as threshods, we identify three types of at each ocation. We further identify the asynchronous co-occurrence ocations with respect to Iowa by measuring the COB and COR of every ocation. To understand the impact of, we identify seven sets of asynchronous co-occurrence ocations with ead-time from 4 to 10 days. Using those three types of, we cacuate 3 7 groups of COB and COR of every ocations. From each group, we first seect a subgroup of ocations whose COR are higher than the average COR and then seect the ocations with top 500 COB of this subgroup as the asynchronous co-occurrence ocations. As a resut, we have identified 21 sets of asynchronous cooccurrence ocations over three different spatia scaes and seven different tempora distances. D. Pattern Learning We consider each EPC as one instance and et the start date of an EPC, t, represent an occurrence of this EPC. Next, we extract those nine atmospheric factors measured at time t from one set of 500 asynchronous co-occurrence ocations as this instance s features. Therefore, there are features incuded in one instance. We generate 21 data sets from those 21 sets of the asynchronous cooccurrence ocations. The number of instances in each data set is the number of EPCs identified in Iowa. Fro
5 Tabe II THE PREDICTION ACCURACY OF THE MODELS USING ASYNCHRONOUS CO-OCCURRENCE LOCATION DATA SETS AND ALL-LOCATION DATA SETS UNDER DIFFERENT LEAD-TIME. Goba Iowa Loca A Avg Loca Iowa Goba A Figure 5. The visuaization of four groups of ocations seected by the Decision Tree modes under three different spatia scaes of Goba, Loca, and Iowa, and by the modes using a ocations. Accuracy Accuracy 4~5 4~6 4~7 4~8 4~9 4~10 Loca Goba Figure 4. The accuracies of those four ensembe modes buit by tempora cascading voting approach. comparison, we aso generate seven data sets each of which has 9 5, 328 features extracted from the 5, 328 ocations and name these data sets the A-ocation data sets. Using the sever fooding records [10], we identify those EPCs covering a food event and abe them as P-EPCs. We aim to earn the circumstances under which contribute to the P-EPCs. Moreover, each data set is spit into a training set and a test set with 2:1 ratio. The Decision Tree mode is used to train on a training set and then test on its corresponding test set. The resuts of the modes of different spatia scaes, and the resuts of the trained mode using a-ocation data sets under different ead-time are isted in Tabe II. Our experiments are conducted on a High Performance Computing Custer managed by the Research Computing Department at the University of Massachusetts Boston. The resuts have shown that using the features extracted from our proposed asynchronous co-occurrence ocations not ony yieds higher or simiar accuracies, but aso dramaticay reduces the execution time, compared using the features extracted form a ocations. In addition, adopting the oca percentie vaues performs the best among the others, because the ocay identified have a better representation of abrupt increases of precipitabe water at one singe ocation than those gobay identified. Iowa A E. Ensembe Modeing We consoidate the four groups, Goba, Loca, Iowa, and A-ocation, of Decision Tree modes (as shown in Tabe II) into four ensembe modes through our proposed Tempora Cascading Voting Ensembe Modeing. In each ensembe mode, every Decision Tree mode has equa voting power and the majority votes is the fina output of this ensembe mode. We vary the number of modes joined to the voting processes and choose these modes based on their ead-time in chronoogica order starting from = 4. The performances of these four ensembe modes buit on different series of ead-time are shown in Figure 4. We have achieved about 87.5% accuracy when predicting the P-EPCs with Loca spatia scae at east 4 days in advance. Since π = 5, we are abe to predict the severe foods caused by heavy precipitation at east 5+4 days in advance with our ensembe mode. F. Seected Location Visuaization For further investigation, we coect a the features seected by the modes under four different spatia scaes: Goba, Loca, Iowa and A-ocation, so we obtain four coections of features. We trace back four groups of ocations from which these four coections features are extracted. We then visuaize these four groups of ocations on the map, shown in Figure 5. With this visuaization, we have uncovered the paths of moving, showing the evidence that the foods coud be foreseen by monitoring the movements of aong these paths. We have aso observed that the ocations near the southern border of the Sahara Desert and the Himaayas mountains are seected by a four type of modes. This observation suggests that the atmospheric changes occurring at the argest desert and the highest mountain might have tight connections with Iowa s severe foods caused by heavy precipitation. IV. RELATED WORK Regression modes are commony used by most researchers to simuate atmospheric circuations for severe weather forecasting [1]. The imitation of these modes is
6 caused by the ampified effect on simuation errors [3], [12]. As a resut, these modes have the acceptabe prediction within five days range. In addition, most of the simuation modes are very computationa expensive. Data mining techniques have been adopted to study extreme weather phenomena and to deepy understand the causa factors of these phenomena [6], [13], [14], [15], having the potentia of deivering ong-ead weather prediction with ess data inputs. Unike the simuation modes, data mining approaches can be appied to predict the occurrences of precipitation bocking having high risks of triggering extreme fooding events [5], [6]. In [6], Wang et a. considered precipitation bocking as tempora custer with a fixed time window size to accumuate precipitation. Unike Wang s approach, our newy defined EPC is of various window sizes. We aso incorporate the historica fooding events in finding the P-EPCs which represent the true nature of heavy precipitation with high potentia of triggering foods. Furthermore, current simuation modes and data mining modes focus on predicting heavy precipitation without further indications what the fooding possibiities are. Instead, our proposed mode is designed to predict the P-EPCs. To achieve oad-ead prediction, Wang et a. set a 15-day windows size for their custers as the ead-time. Different from their approach, we propose STACOP to extend the ead-time by connecting heavy precipitation to as the causa factors. From feature seection point of view, other approaches incude the entire feature space during the modeing process. Through our proposed STACOP, we preprocess the data to identify the asynchronous co-occurrence ocations for feature extraction before buiding the modes. As a resut, we dramaticay reduce the training date size and computationa cost. V. CONCLUSION The goa of this study is to hep the domain scientists deveoping eary food warning systems from machine earning perspective, which can accuratey forecast extreme foods caused by heavy precipitation at east 9 days in advance at a goba eve. Our proposed framework is designed to be scaabe and for distributed computing so it can aso be appied to other spatio-tempora reated big data anaytics, such as droughts or hurricanes prediction, in our future work. ACKNOWLEDGMENT The authors woud ike to acknowedge Research Computing Department at the University of Massachusetts Boston for providing the support and the use of the supercomputing faciities. We aso thank the TCL Research America for the funding. REFERENCES [1] H. Coke and F. Pappenberger, Ensembe food forecasting: a review, Journa of Hydroogy, vo. 375, no. 3, pp , [2] D. J. Stensrud, J.-W. Bao, and T. T. Warner, Using initia condition and mode physics perturbations in shortrange ensembe simuations of mesoscae convective systems, Monthy Weather Review, vo. 128, no. 7, pp , [3] L. Afieri, P. Saamon, F. Pappenberger, F. Wetterha, and J. Thieen, Operationa eary warning systems for waterreated hazards in europe, Environmenta Science & Poicy, vo. 21, pp , [4] Nationa Center for Atmospheric Research. (2015, Jan.) 2014 ncar annua report. [Onine]. Avaiabe: /ncar/2014-ncar-annua-report [5] X. Gao, C. A. Schosser, P. Xie, E. Monier, and D. Entekhabi, An anaogue approach to identify extreme precipitation events: Evauation and appication to cmip5 cimate modes in the united states, MIT Joint Program, Tech. Rep., [6] D. Wang, W. Ding, K. Yu, X. Wu, P. Chen, D. L. Sma, and S. Isam, Towards ong-ead forecasting of extreme food events: A data mining framework for precipitation custer precursors identification, in Proceedings of the 19th ACM SIGKDD Internationa Conference on Knowedge Discovery and Data Mining, ser. KDD 13. New York, NY, USA: ACM, 2013, pp [Onine]. Avaiabe: [7] G. Lenderink and E. Van Meijgaard, Increase in houry precipitation extremes beyond expectations from temperature changes, Nature Geoscience, vo. 1, no. 8, pp , [8] K. E. Trenberth, The impact of cimate change and variabiity on heavy precipitation, foods, and droughts, Encycopedia of hydroogica sciences, vo. 17, [9] M. D. King, W. P. Menze, Y. J. Kaufman, D. Tanré, B.-C. Gao, S. Patnick, S. A. Ackerman, L. A. Remer, R. Pincus, and P. A. Hubanks, Coud and aeroso properties, precipitabe water, and profies of temperature and water vapor from modis, Geoscience and Remote Sensing, IEEE Transactions on, vo. 41, no. 2, pp , [10] Iowa Homeand Security and Emergency Management, Iowa disaster history, iowa disaster history.htm, accessed: [11] E. Kanay, M. Kanamitsu, R. Kister, W. Coins, D. Deaven, L. Gandin, M. Irede, S. Saha, G. White, J. Wooen et a., The ncep/ncar 40-year reanaysis project, Buetin of the American meteoroogica Society, vo. 77, no. 3, pp , [12] J. Lubchenco and T. R. Kar, Predicting and managing extreme weather events, Physics Today, vo. 65, no. 3, p. 31, [13] X. Li, B. Pae, N. Vijayakumar, R. Ramachandran, S. Graves, and H. Conover, Rea-time storm detection and weather forecast activation through data mining and events processing, Earth Science Informatics, vo. 1, no. 2, pp , [14] T. A. Supinie, A. McGovern, J. Wiiams, and J. Abernathy, Spatiotempora reationa random forests, in Data Mining Workshops, ICDMW 09. IEEE Internationa Conference on. IEEE, 2009, pp [15] A. McGovern, D. John Gagne, N. Troutman, R. A. Brown, J. Basara, and J. K. Wiiams, Using spatiotempora reationa random forests to improve our understanding of severe weather processes, Statistica Anaysis and Data Mining, vo. 4, no. 4, pp , 2011.
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