kdd_coil_train-2.arff

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UCI/numeric/kdd_coil_train-2.arff
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2009-11-03 15:24:30
Description

The data contains measurements of river chemical concentrations and algae densities. This data set is from the 1999 Computational Intelligence and Learning (COIL) competition.
File contents

%%%%%%%%%%%%%%%%%%%%
% Data-Description %
%%%%%%%%%%%%%%%%%%%%
%
%                           COIL 1999 Competition Data
% 
% Data Type
% 
%    multivariate
% 
% Abstract
% 
%    This data set is from the 1999 Computational Intelligence and Learning
%    (COIL) competition. The data contains measurements of river chemical
%    concentrations and algae densities.
% 
% Sources
% 
%     Original Owner
% 
% [1]ERUDIT
% European Network for Fuzzy Logic and Uncertainty Modelling
% in Information Technology
% 
%     Donor
% 
% Jens Strackeljan
% Technical University Clausthal
% Institute of Applied Mechanics
% Graupenstr. 3, 38678 Clausthal-Zellerfeld, Germany
% [2]tmjs@itm.tu-clausthal.de
% 
%    Date Donated: September 9, 1999
% 
% Data Characteristics
% 
%    This data comes from a water quality study where samples were taken
%    from sites on different European rivers of a period of approximately
%    one year. These samples were analyzed for various chemical substances
%    including: nitrogen in the form of nitrates, nitrites and ammonia,
%    phosphate, pH, oxygen, chloride. In parallel, algae samples were
%    collected to determine the algae population distributions.
% 
% Other Relevant Information
% 
%    The competition involved the prediction of algal frequency
%    distributions on the basis of the measured concentrations of the
%    chemical substances and the global information concerning the season
%    when the sample was taken, the river size and its flow velocity. The
%    competition [3]instructions contain additional information on the
%    prediction task.
% 
% Data Format
% 
%    There are a total of 340 examples each containing 17 values. The first
%    11 values of each data set are the season, the river size, the fluid
%    velocity and 8 chemical concentrations which should be relevant for
%    the algae population distribution. The last 8 values of each example
%    are the distribution of different kinds of algae. These 8 kinds are
%    only a very small part of the whole community, but for the competition
%    we limited the number to 7. The value 0.0 means that the frequency is
%    very low. The data set also contains some empty fields which are
%    labeled with the string XXXXX.
% 
%    The training data are saved in the file: analysis.data (ASCII format).
% 
%    Table 1: Structure of the file analysis.data
% 
%    A
% 
% 
%    K
% 
%    a
% 
% 
%    g
% 
%    CC[1,1]
% 
% 
%    CC[1,11]
% 
%    AG[1,1]
% 
% 
%    AG[1,7]
% 
%    CC[200,1]
% 
% 
%    CC[200,11]
% 
%    AG[200,1]
% 
% 
%    AG[200,7]
% 
%    Explanation:
%    CC[i,j]: Chemical concentration or river characteristic
%    AG[i,j]: Algal frequency
% 
%    The chemical parameters are labeled as A, ..., K. The columns of the
%    algaes are labeled as a, ..,g.
% 
% Past Usage
% 
%    [4]The Third (1999) International COIL Competition Home Page
%      _________________________________________________________________
% 
% 
%     [5]The UCI KDD Archive
%     [6]Information and Computer Science
%     [7]University of California, Irvine
%     Irvine, CA 92697-3425
% 
%    Last modified: October 13, 1999
% 
% References
% 
%    1. http://www.erudit.de/
%    2. mailto:tmjs@itm.tu-clausthal.de
%    3. file://localhost/research/ml/datasets/uci/raw/data/ucikdd/coil/instructions.txt
%    4. http://www.erudit.de/erudit/activities/ic-99/index.htm
%    5. http://kdd.ics.uci.edu/
%    6. http://www.ics.uci.edu/
%    7. http://www.uci.edu/
%
%%%%%%%%%%%%%%%%%%%%
% Task-Description %
%%%%%%%%%%%%%%%%%%%%
%
% 
% Third International Competition 
% 
% Protecting rivers and streams by monitoring chemical concentrations and 
% algae communities.
% 
% 
% Intelligent Techniques for Monitoring Water Quality using chemical
% indicators and algae population
% 
% Recent years have been characterised by increasing concern at the
% impact man is having on the environment.  
% The impact on the environment of toxic waste, from a wide variety
% of manufacturing processes, is well known. More recently, however,
% it has become clear that the more subtle effects of nutrient level
% and chemical balance changes arising from farming land run-off and
% sewage water treatment also have a serious, but indirect, effect on
% the states of rivers, lakes and even the sea.  In temperate climates
% across the world summers are characterized by numerous reports excessive
% summer algae growth resulting in poor water clarity, mass deaths of
% river fish from reduced oxygen levels and the closure of recreational
% water facilities on account of the toxic effects of this annual algal bloom.
% Reducing the impact of these man-made changes in river nutrient levels
% has stimulated much biological research with the aim of identifying
% the crucial chemical control variables for the biological
% processes.  
% 
% The data used in this problem comes from one such study.
% During the research study water quality samples were
% taken from sites on different European rivers of a period of
% approximately one year.  These samples were analyzed for various
% chemical substances including: nitrogen in the form of nitrates,
% nitrites and ammonia, phosphate, pH, oxygen, chloride.
% In parallel, algae samples were collected to determine the algae population
% distributions. It is well known that the dynamics of the
% algae community is determined by external chemical 
% environment with one or more factors being predominant.
% While the chemical analysis is cheap and easily 
% automated, the biological part involves microscopic examination,
% requires trained manpower and is therefore both 
% expensive and slow. 
% 
% Diatoms like Cymbella are major contributors to primary production
% throughout the world. The diatom reacts with 
% large sensitivity to even small changes in acidity . 
% 
% Over a three and half billion year history algae have evolved and
% adapted as primary plant colonizers of almost 
% every known habitant in terrestrial and aquatic environments.
% They respond very rapidly to man-made environment changes.
% 
% 
% 
% The relationship between the chemical and biological features is
% complex and can be expected to need the application of advanced
% techniques. Typical of such real-life problems, the particular
% data set for the problem contains a mixture of (fuzzy) qualiative
% variables and numerical measurement values, with much of the data
% being incomplete.
% 
% The competition task is the prediction of algal frequency distributions
% on the basis of  the measured concentrations of the chemical
% substances and the global information concerning the season when the sample
% was taken, the river size and its flow velocity. The two last variables
% are given as linguistic variables.
% 
% 340 data sets were taken and each contain 17 values. The
% first 11 values of each data set are the season, the river
% size, the fluid velocity and 8 chemical concentrations which
% should be relevant for the algae population distribution.
% The last 8 values of each data set are the distribution of
% different kinds of algae. These 8 kinds are only a very small
% part of the whole community, but for the competition we limited
% the number to 7. The value 0.0 means that the frequency is very low.
% The data set also contains some empty fields which are labeled
% with the string XXXXX.
% 
% Each participant in the competition receives 200 complete data sets
% (training data) and 140 data sets (evaluation data) containing only
% the 11 values of the river descriptions and the chemical concentrations.
% 
% This training data is to be used in obtainin
% a 'model' providing a prediction of the algal distributions associated
% with the evaluation data.
% 
% 
% 
% The training data are saved in the file:
% 
% analysis.txt (ASCII format).
% 
% Structure of the file analysis.txt
% 
% A                          K              a                   g
% CC1,1   ...                CC1,11         AG1,1    ...        AG1,7
% ....                        ...            ...                 ...
% 
% 
% CC200,1 ...                CC200,11       AG240,1  ...        AG240,7
% 
% 
% Explanation:
% CCi,j:  Chemical concentration    j=1,..11
% AGi,k: Algal frequency            k=1...7
% 
% 
% The chemical parameters are labeled as A, ..., K.
% The columns of the algaes are labeled as a, ..,g.
% 
% 
% Evaluation data are saved in file eval.txt (ASCII format).
% 
% 
% Table 2: Structure of the file eval.* 
% A                               K
% CC1,1     ...                   CC1,11
% 
% .....                            ...
%                                 
% CC140,1   ...                   CC140,11
% 
% _____________________________________________________________
% 
% Objective 
% 
% The objective of the competition is to provide a prediction
% model on basis of the training data. Having obtained this
% prediction model, each participant must provide the solution
% in the form of the results of applying this model to the
% evaluation data. The results obtained in this way should
% correspond to the results of the evaluation data
% (which are known to the organizer). The criteria used to evaluate
% the results is given below.
% All 7 Algae frequency distributions must be determined.
% For this purpose any number of partial models may be developed.
% 
% _____________________________________________________________
% 
% Judgment of the results
% 
% To judge the results, the sum of squared errors will be calculated.
% The following Table describes the results of a particular participant.
% 
% Matrix of results
% a                     g
% 
% Res1,1   ...          Res1,7
% 
% ....                   ...
% 
% Res140,1              Res140,7
% 
% 
% All solutions that lead to a smallest total error will
% be regarded as winner of the contest. 
% 
% 
%
% Information about the dataset
% CLASSTYPE: numeric
% CLASSINDEX: last
%
% ALGAE #: 2/7

@relation coil-2

@attribute season {autumn,spring,summer,winter}
@attribute river_size {large_,medium,small_}
@attribute fluid_velocity {high__,low___,medium}
@attribute concentration_1 REAL
@attribute concentration_2 REAL
@attribute concentration_3 REAL
@attribute concentration_4 REAL
@attribute concentration_5 REAL
@attribute concentration_6 REAL
@attribute concentration_7 REAL
@attribute concentration_8 REAL
@attribute algae_2 REAL

@data
winter,small_,medium,8.00000,9.80000,60.80000,6.23800,578.00000,105.00000,170.00000,50.00000,0.00000
spring,small_,medium,8.35000,8.00000,57.75000,1.28800,370.00000,428.75000,558.75000,1.30000,7.60000
autumn,small_,medium,8.10000,11.40000,40.02000,5.33000,346.66699,125.66700,187.05701,15.60000,53.60000
spring,small_,medium,8.07000,4.80000,77.36400,2.30200,98.18200,61.18200,138.70000,1.40000,41.00000
autumn,small_,medium,8.06000,9.00000,55.35000,10.41600,233.70000,58.22200,97.58000,10.50000,2.90000
winter,small_,high__,8.25000,13.10000,65.75000,9.24800,430.00000,18.25000,56.66700,28.40000,14.60000
summer,small_,high__,8.15000,10.30000,73.25000,1.53500,110.00000,61.25000,111.75000,3.20000,1.20000
autumn,small_,high__,8.05000,10.60000,59.06700,4.99000,205.66701,44.66700,77.43400,6.90000,1.60000
winter,small_,medium,8.70000,3.40000,21.95000,0.88600,102.75000,36.30000,71.00000,5.54400,5.40000
winter,small_,high__,7.93000,9.90000,8.00000,1.39000,5.80000,27.25000,46.60000,0.80000,0.00000
spring,small_,high__,7.70000,10.20000,8.00000,1.52700,21.57100,12.75000,20.75000,0.80000,0.00000
summer,small_,high__,7.45000,11.70000,8.69000,1.58800,18.42900,10.66700,19.00000,0.60000,0.00000
winter,small_,high__,7.74000,9.60000,5.00000,1.22300,27.28600,12.00000,17.00000,41.00000,0.00000
summer,small_,high__,7.72000,11.80000,6.30000,1.47000,8.00000,16.00000,15.00000,0.50000,1.00000
winter,small_,high__,7.90000,9.60000,3.00000,1.44800,46.20000,13.00000,61.60000,0.30000,5.00000
autumn,small_,high__,7.55000,11.50000,4.70000,1.32000,14.75000,4.25000,98.25000,1.10000,0.00000
winter,small_,high__,7.78000,12.00000,7.00000,1.42000,34.33300,18.66700,50.00000,1.10000,0.00000
spring,small_,high__,7.61000,9.80000,7.00000,1.44300,31.33300,20.00000,57.83300,0.40000,0.00000
summer,small_,high__,7.35000,10.40000,7.00000,1.71800,49.00000,41.50000,61.50000,0.80000,0.00000
spring,small_,high__,7.20000,9.20000,0.80000,0.64200,81.00000,15.60000,18.00000,0.50000,0.00000
autumn,small_,high__,7.75000,10.30000,32.92000,2.94200,42.00000,16.00000,40.00000,7.60000,0.00000
winter,small_,high__,7.62000,8.50000,11.86700,1.71500,208.33299,3.00000,27.50000,1.70000,0.00000
spring,small_,high__,7.84000,9.40000,10.97500,1.51000,12.50000,3.00000,11.50000,1.50000,8.60000
summer,small_,high__,7.77000,10.70000,12.53600,3.97600,58.50000,9.00000,44.13600,3.00000,0.00000
winter,small_,high__,7.09000,8.40000,10.50000,1.57200,28.00000,4.00000,13.60000,0.50000,0.00000
autumn,small_,high__,6.80000,11.10000,9.00000,0.63000,20.00000,4.00000,?,2.70000,1.90000
winter,small_,high__,8.00000,9.80000,16.00000,0.73000,20.00000,26.00000,45.00000,0.80000,0.00000
spring,small_,high__,7.20000,11.30000,9.00000,0.23000,120.00000,12.00000,19.00000,0.50000,1.00000
autumn,small_,high__,7.40000,12.50000,13.00000,3.33000,60.00000,72.00000,142.00000,4.90000,16.00000
winter,small_,high__,8.10000,10.30000,26.00000,3.78000,60.00000,246.00000,304.00000,2.80000,17.10000
summer,small_,high__,7.80000,11.30000,20.08300,3.02000,49.50000,53.00000,130.75000,5.80000,8.00000
winter,small_,high__,8.30000,10.90000,1.17000,0.73500,13.50000,1.62500,3.00000,0.20000,0.00000
spring,small_,high__,8.00000,?,1.45000,0.81000,10.00000,2.50000,3.00000,0.30000,0.00000
winter,small_,medium,8.30000,8.90000,20.62500,3.41400,228.75000,196.62000,253.25000,12.32000,38.50000
spring,small_,medium,8.10000,10.50000,22.28600,4.07100,178.57001,182.42000,255.28000,8.95700,2.70000
winter,small_,medium,8.00000,5.50000,77.00000,6.09600,122.85000,143.71001,296.00000,3.70000,5.90000
summer,small_,medium,8.15000,7.10000,54.19000,3.82900,647.57001,59.42900,175.04601,13.20000,0.00000
winter,small_,high__,8.30000,7.70000,50.00000,8.54300,76.00000,264.89999,344.60001,22.50000,40.90000
spring,small_,high__,8.30000,8.80000,54.14300,7.83000,51.42900,276.85001,326.85699,11.84000,3.10000
winter,small_,high__,8.40000,13.40000,69.75000,4.55500,37.50000,10.00000,40.66700,3.90000,4.10000
spring,small_,high__,8.30000,12.50000,87.00000,4.87000,22.50000,27.00000,43.50000,3.30000,1.00000
autumn,small_,high__,8.00000,12.10000,66.30000,4.53500,39.00000,16.00000,39.00000,0.80000,3.40000
winter,small_,low___,?,12.60000,9.00000,0.23000,10.00000,5.00000,6.00000,1.10000,0.00000
spring,small_,medium,7.60000,9.60000,15.00000,3.02000,40.00000,27.00000,121.00000,2.80000,0.00000
autumn,small_,medium,7.29000,11.21000,17.75000,3.07000,35.00000,13.00000,20.81200,12.10000,7.40000
winter,small_,medium,7.60000,10.20000,32.30000,4.50800,192.50000,12.75000,49.33300,7.90000,0.00000
summer,small_,medium,8.00000,7.90000,27.23300,1.65100,28.33300,7.30000,22.90000,4.50000,0.00000
winter,small_,high__,7.90000,11.00000,6.16700,1.17200,18.33300,7.75000,11.80000,0.50000,0.00000
spring,small_,high__,7.90000,9.00000,5.27300,0.91000,33.63600,9.00000,11.81800,0.80000,0.00000
winter,small_,high__,6.60000,10.80000,?,3.24500,10.00000,1.00000,6.50000,?,0.00000
spring,small_,medium,5.60000,11.80000,?,2.22000,5.00000,1.00000,1.00000,?,0.00000
autumn,small_,medium,5.70000,10.80000,?,2.55000,10.00000,1.00000,4.00000,?,4.60000
spring,small_,high__,6.60000,9.50000,?,1.32000,20.00000,1.00000,6.00000,?,0.00000
summer,small_,high__,6.60000,10.80000,?,2.64000,10.00000,2.00000,11.00000,?,0.00000
autumn,small_,medium,6.60000,11.30000,?,4.17000,10.00000,1.00000,6.00000,?,0.00000
spring,small_,medium,6.50000,10.40000,?,5.97000,10.00000,2.00000,14.00000,?,0.00000
summer,small_,medium,6.40000,?,?,?,?,?,14.00000,?,0.00000
autumn,small_,high__,7.83000,11.70000,4.08300,1.32800,18.00000,3.33300,6.66700,?,0.00000
spring,small_,high__,7.57000,10.80000,4.57500,1.20300,27.50000,2.00000,6.75000,1.00000,4.30000
summer,small_,high__,7.19000,11.70000,4.32600,1.47400,160.00000,2.50000,7.20000,0.30000,1.70000
winter,small_,high__,7.44000,10.10000,2.93300,0.77000,15.00000,1.33300,6.00000,0.60000,0.00000
spring,small_,high__,7.14000,9.80000,3.27500,0.92300,15.00000,1.25000,10.75000,2.50000,0.00000
summer,small_,high__,7.00000,12.10000,3.13600,1.20800,16.20000,1.80000,2.50000,0.50000,0.00000
autumn,medium,medium,8.50000,8.10000,38.12500,3.85000,225.00000,45.00000,152.33299,5.20000,1.70000
summer,medium,medium,7.92500,10.20000,34.03700,9.08000,109.00000,55.00000,58.62300,11.60000,4.00000
winter,medium,medium,8.10000,8.10000,136.00000,3.77300,245.00000,136.75000,249.25000,20.87000,5.80000
spring,medium,medium,8.20000,6.80000,129.37500,3.31600,271.25000,100.00000,233.50000,13.00000,8.00000
spring,medium,high__,9.10000,9.40000,35.75000,5.16400,32.50000,85.50000,215.50000,18.37000,9.60000
autumn,medium,medium,8.10000,9.80000,29.50000,1.28700,224.28600,25.16700,102.33300,3.60000,1.00000
winter,medium,medium,8.00000,5.90000,27.40000,0.73500,133.63600,36.00000,105.72700,3.00000,7.30000
spring,medium,medium,8.00000,3.30000,26.76000,0.65800,165.00000,37.37500,111.37500,3.00000,0.00000
winter,medium,high__,7.50000,9.20000,11.00000,3.31000,101.00000,26.60000,108.00000,1.30000,0.00000
spring,medium,high__,7.40000,9.80000,11.00000,3.23500,255.00000,38.75000,56.66700,2.00000,0.00000
autumn,medium,high__,7.30000,11.70000,10.40000,4.93000,130.00000,10.80000,60.00000,4.30000,0.00000
winter,medium,high__,7.40000,8.90000,13.50000,5.44200,123.33300,27.66700,104.00000,21.00000,4.30000
summer,medium,high__,7.40000,11.17000,12.14600,6.18800,89.60000,32.00000,69.93000,3.10000,4.10000
autumn,medium,medium,7.50000,10.80000,31.00000,4.40800,737.50000,111.25000,214.00000,2.90000,0.00000
winter,medium,medium,7.60000,6.00000,53.00000,3.73400,914.00000,137.60001,254.60001,4.30000,0.00000
summer,medium,medium,7.40000,10.77000,36.24800,3.73000,429.20001,57.60000,169.00101,3.20000,0.00000
winter,medium,medium,8.50000,8.60000,125.60000,3.77800,124.16700,197.83299,303.33301,40.00000,15.20000
spring,medium,medium,8.70000,9.40000,173.75000,3.31800,101.25000,267.75000,391.75000,3.50000,5.50000
summer,medium,medium,8.10000,10.70000,94.40500,4.69800,153.00000,191.75000,265.25000,7.30000,2.10000
winter,medium,high__,8.80000,8.50000,53.33300,5.13200,96.66700,120.50000,232.83299,31.00000,5.60000
spring,medium,high__,7.80000,10.50000,70.00000,2.44300,98.33300,144.66701,244.00000,9.00000,3.10000
summer,medium,high__,7.90000,11.80000,63.51000,4.94000,137.00000,159.50000,218.00000,6.50000,5.20000
autumn,medium,low___,8.50000,10.50000,56.71700,0.33000,215.71400,23.00000,138.50000,20.82900,0.00000
winter,medium,low___,9.10000,5.40000,61.05000,0.30800,105.55600,104.22200,239.00000,72.47800,31.90000
spring,medium,low___,8.90000,4.50000,57.75000,0.26700,155.00000,97.33300,235.66701,98.81700,16.20000
winter,medium,high__,7.90000,6.30000,101.87500,3.97800,153.75000,51.75000,205.87500,2.00000,2.10000
summer,medium,high__,7.80000,8.20000,85.98200,6.20000,421.66699,31.33300,211.66701,21.90000,3.40000
winter,medium,medium,7.70000,7.10000,63.62500,3.14000,122.50000,28.62500,186.50000,30.00000,2.10000
spring,medium,medium,7.80000,6.50000,82.11100,2.60300,215.55600,12.88900,154.12500,5.20000,0.00000
winter,medium,low___,7.70000,5.30000,65.33300,2.89900,371.11099,51.11100,183.66701,17.20000,0.00000
summer,medium,low___,7.50000,8.80000,58.33100,8.68800,758.75000,104.50000,292.62500,3.00000,0.00000
autumn,medium,low___,7.60000,10.00000,49.62500,5.45600,308.75000,38.62500,285.71399,75.00000,21.60000
winter,medium,low___,8.70000,7.40000,47.77800,2.31600,38.11100,24.66700,201.77800,3.00000,5.40000
summer,medium,low___,7.70000,11.10000,47.22900,8.75900,239.00000,54.00000,275.14301,65.70000,19.60000
autumn,medium,high__,8.30000,11.10000,41.50000,4.66500,931.83301,39.00000,124.20000,13.10000,13.70000
winter,medium,high__,8.43000,6.00000,40.16700,2.67000,723.66699,60.83300,141.83299,25.00000,6.40000
summer,medium,high__,8.16000,11.10000,32.05600,5.69400,461.87500,71.00000,132.54601,15.00000,38.80000
winter,medium,high__,8.70000,9.80000,5.88900,1.53400,51.11100,9.66700,17.33300,1.00000,1.50000
spring,medium,high__,8.20000,11.30000,7.25000,1.87500,25.00000,6.50000,26.00000,0.30000,0.00000
summer,medium,high__,8.50000,11.80000,7.83800,1.73200,206.53799,8.69200,16.66200,2.10000,0.00000
spring,medium,medium,7.80000,6.00000,53.42500,0.38100,118.57100,37.85700,102.57100,1.20000,1.40000
summer,medium,medium,8.00000,9.70000,57.84800,0.46100,217.75000,37.00000,86.99700,3.00000,14.50000
winter,medium,high__,9.70000,10.80000,0.22200,0.40600,10.00000,22.44400,10.11100,?,1.50000
summer,medium,high__,8.60000,11.62000,1.54900,0.44500,25.83300,16.83300,18.29300,1.40000,0.00000
autumn,medium,medium,8.30000,11.60000,5.83000,0.70100,12.72700,3.54500,13.20000,3.20000,0.00000
spring,medium,low___,8.40000,5.30000,74.66700,3.90000,131.66701,261.60001,432.90900,24.91700,12.70000
summer,medium,low___,8.20000,6.60000,131.39999,4.18800,92.00000,238.20000,320.39999,6.80000,1.90000
winter,medium,medium,8.20000,9.40000,45.27300,7.19500,345.45499,144.00000,287.00000,9.88200,18.40000
spring,medium,medium,8.10000,7.10000,42.63600,5.07800,56.36400,166.72701,262.72699,17.20000,8.90000
summer,medium,medium,8.10000,9.00000,48.42900,6.64000,128.57100,181.00000,222.28600,6.42900,11.60000
winter,medium,high__,7.40000,10.70000,11.81800,2.16300,170.90900,36.90900,122.00000,5.55500,0.00000
spring,medium,high__,8.30000,9.70000,10.55600,1.92100,65.55600,61.55600,127.22200,5.23300,0.00000
summer,medium,high__,8.60000,10.70000,12.00000,2.23100,43.75000,62.62500,89.62500,2.15000,0.00000
winter,medium,medium,9.10000,11.60000,31.09100,5.09900,246.36400,55.00000,284.00000,88.25500,36.60000
spring,medium,medium,9.00000,6.90000,28.33300,2.95400,76.66700,102.33300,277.33301,110.45600,16.40000
summer,medium,medium,8.30000,10.00000,30.12500,3.72600,102.50000,75.87500,177.62500,50.22500,32.80000
winter,medium,high__,8.50000,10.10000,10.93600,1.33500,236.00000,34.63600,72.90000,11.10000,0.00000
spring,medium,high__,8.30000,7.70000,10.07800,1.21200,103.33300,48.66700,82.44400,2.00000,0.00000
summer,medium,high__,7.30000,10.50000,11.08800,1.37400,92.37500,48.62500,66.75000,3.30000,0.00000
spring,medium,medium,7.90000,8.30000,391.50000,6.04500,380.00000,173.00000,317.00000,5.50000,1.70000
autumn,medium,medium,8.00000,11.90000,130.67000,6.54000,196.00000,75.00000,84.00000,4.50000,8.70000
spring,medium,medium,8.00000,9.20000,39.00000,4.86000,120.00000,187.00000,213.00000,2.00000,26.50000
autumn,medium,medium,8.10000,11.70000,35.66000,5.13000,46.50000,49.00000,88.50000,2.50000,72.60000
winter,medium,low___,8.43000,9.90000,37.60000,0.82600,124.00000,32.50000,115.00000,11.70000,2.90000
summer,medium,low___,8.10000,6.20000,39.00000,0.67300,112.85700,60.00000,98.14300,2.00000,0.00000
winter,medium,medium,7.90000,11.20000,49.90000,9.77300,505.00000,67.50000,143.75000,5.45000,2.60000
summer,medium,medium,8.10000,6.20000,51.11300,5.09900,175.00000,132.50000,197.14301,6.40000,15.70000
spring,medium,high__,7.80000,9.50000,8.30000,1.67000,34.00000,16.80000,35.20000,1.00000,0.00000
autumn,medium,high__,7.90000,10.50000,10.20700,2.30400,132.25000,10.58300,23.48500,2.00000,0.00000
winter,medium,low___,8.00000,4.50000,79.07700,8.98400,920.00000,70.00000,200.23100,19.40000,1.40000
spring,medium,low___,7.60000,6.30000,81.33300,9.71500,196.66701,77.33300,147.83299,3.00000,11.20000
winter,medium,high__,8.22000,8.10000,41.25000,1.41500,172.50000,46.66700,123.33300,30.40000,12.70000
autumn,medium,high__,8.30000,9.90000,40.22600,1.58700,235.00000,33.80000,75.20700,23.80000,28.00000
winter,medium,high__,8.47000,9.00000,46.16700,2.10200,84.66700,48.00000,116.20000,7.30000,16.00000
spring,medium,high__,8.40000,4.90000,47.00000,0.53600,91.83300,109.00000,188.66701,32.00000,25.40000
autumn,medium,high__,8.87000,11.00000,41.16300,2.27300,54.75000,39.00000,72.69600,22.70000,5.60000
summer,medium,high__,7.70000,4.40000,53.00000,2.31000,90.00000,22.20000,116.20000,16.00000,0.00000
spring,medium,medium,7.90000,6.00000,127.83300,2.68000,176.66701,27.50000,76.33300,2.10000,21.50000
autumn,medium,medium,7.80000,10.53000,100.83000,5.41000,486.50000,24.00000,58.37400,27.50000,1.90000
spring,large_,low___,8.60000,3.60000,50.00000,0.37600,134.00000,54.10000,125.80000,26.80000,28.00000
autumn,large_,low___,8.40000,10.60000,19.22000,1.65500,96.83300,20.66700,54.91600,20.60000,11.30000
winter,large_,low___,8.30000,11.50000,26.00000,1.87000,62.50000,30.75000,75.33300,34.75000,20.10000
spring,large_,low___,9.00000,5.80000,?,0.90000,142.00000,102.00000,186.00000,68.05000,20.60000
spring,large_,low___,9.50000,5.70000,44.00000,0.10200,146.66701,151.33299,252.50000,93.68300,21.70000
summer,large_,low___,8.80000,8.80000,43.00000,0.13000,103.33300,180.66701,269.66699,92.66700,28.20000
autumn,large_,low___,8.84000,12.90000,43.09000,0.84600,52.20000,8.60000,46.43800,81.54000,21.50000
winter,large_,high__,7.30000,9.90000,16.00000,4.82000,101.66700,14.66700,85.00000,2.00000,0.00000
autumn,large_,high__,7.40000,10.68000,22.35000,5.41400,244.60001,66.40000,171.27200,3.80000,0.00000
spring,large_,low___,9.10000,4.30000,82.85700,0.86000,137.27299,102.36400,232.89999,54.36700,6.00000
autumn,large_,low___,8.53000,11.10000,63.29200,1.72600,227.60001,84.30000,146.45200,21.22000,14.70000
winter,large_,low___,8.56000,8.70000,43.97000,4.05300,643.00000,221.89999,246.66701,14.70000,2.10000
autumn,large_,low___,8.06000,8.30000,38.90200,3.67800,627.27301,205.63600,219.90900,6.20900,0.00000
winter,large_,medium,8.21000,9.30000,104.81800,3.90800,124.36400,82.22200,167.89999,5.60900,4.60000
spring,large_,medium,8.50000,7.30000,71.44400,2.51200,66.66700,64.38900,137.77800,9.38400,3.80000
spring,large_,medium,8.60000,10.60000,208.36400,4.45900,197.90900,87.33300,194.10001,27.61800,1.20000
winter,large_,medium,9.06000,6.35000,187.18300,3.35100,54.77800,159.16701,221.27800,20.80000,21.10000
autumn,large_,high__,8.70000,10.70000,4.54500,0.94100,32.72700,16.00000,21.30000,1.10000,0.00000
spring,large_,high__,8.10000,10.70000,3.50000,1.01300,12.50000,12.75000,11.00000,0.60000,9.70000
summer,large_,high__,8.40000,10.29000,5.32600,0.99600,53.84600,7.66700,14.35400,0.80000,7.50000
spring,large_,medium,8.60000,10.10000,2.11100,0.66300,11.11100,3.22200,7.00000,1.30000,2.00000
summer,large_,medium,8.20000,9.50000,2.20000,0.67200,10.00000,3.80000,6.20000,0.80000,3.80000
winter,large_,medium,8.50000,10.50000,2.75000,0.75800,10.50000,4.00000,7.65400,4.00000,5.00000
summer,large_,medium,8.30000,10.00000,3.86000,0.86600,32.00000,6.00000,16.00000,2.86000,6.70000
winter,large_,high__,8.00000,10.90000,9.05500,0.82500,40.00000,21.08300,56.09100,?,19.60000
summer,large_,high__,8.10000,10.20000,7.61300,0.69900,32.50000,26.62500,52.87500,2.00000,1.70000
winter,large_,low___,8.70000,10.80000,39.10900,6.22500,161.81799,104.72700,228.36400,46.07500,3.90000
winter,large_,low___,8.70000,11.70000,22.45500,3.76500,88.18200,41.30000,85.40000,17.49100,4.70000
summer,large_,low___,8.40000,8.20000,23.25000,2.80500,43.75000,51.12500,87.12500,14.77500,12.00000
autumn,large_,low___,8.55000,11.00000,22.32000,3.14000,82.10000,45.90000,101.45500,18.33000,7.00000
spring,large_,medium,8.50000,7.60000,12.77800,1.87300,17.77800,50.88900,127.00000,24.55600,0.00000
autumn,large_,medium,8.70000,11.40000,15.54100,2.32300,103.00000,34.50000,81.55800,5.62000,0.00000
winter,large_,medium,8.40000,10.50000,12.18200,1.51900,65.45500,19.72700,50.45500,8.15500,4.60000
spring,large_,medium,8.20000,8.20000,7.33300,1.00300,37.77800,19.11100,120.88900,5.11100,12.70000
autumn,large_,medium,8.58000,11.10000,23.82500,3.61700,72.60000,51.11100,91.11100,22.90000,22.00000
summer,large_,medium,8.50000,7.90000,12.44400,2.58600,96.66700,19.11100,61.44400,6.16700,13.20000
autumn,large_,medium,8.40000,8.40000,17.37500,3.83300,83.75000,53.62500,79.75000,2.33800,21.70000
spring,large_,medium,8.30000,10.60000,14.32000,3.20000,125.33300,35.33300,75.90400,4.66700,7.00000
autumn,large_,medium,8.20000,7.00000,139.98900,2.97800,60.11000,78.33300,140.22000,31.73800,15.90000
winter,large_,medium,8.00000,7.60000,?,?,?,?,?,?,12.50000
summer,large_,medium,8.50000,6.70000,82.85200,2.80000,27.06900,64.00000,140.51700,18.30000,10.50000

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