"%%%%%%%%%%%%%%%%%% 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 #: 3/7" "0" "coil-3" 8 8.35 8.1 8.07 8.06 8.25 8.15 8.05 8.7 7.93 7.7 7.45 7.74 7.72 7.9 7.55 7.78 7.61 7.35 7.2 7.75 7.62 7.84 7.77 7.09 6.8 8 7.2 7.4 8.1 7.8 8.3 8 8.3 8.1 8 8.15 8.3 8.3 8.4 8.3 8 nan 7.6 7.29 7.6 8 7.9 7.9 6.6 5.6 5.7 6.6 6.6 6.6 6.5 6.4 7.83 7.57 7.19 7.44 7.14 7 8.5 7.925 8.1 8.2 9.1 8.1 8 8 7.5 7.4 7.3 7.4 7.4 7.5 7.6 7.4 8.5 8.7 8.1 8.8 7.8 7.9 8.5 9.1 8.9 7.9 7.8 7.7 7.8 7.7 7.5 7.6 8.7 7.7 8.3 8.43 8.16 8.7 8.2 8.5 7.8 8 9.7 8.6 8.3 8.4 8.2 8.2 8.1 8.1 7.4 8.3 8.6 9.1 9 8.3 8.5 8.3 7.3 7.9 8 8 8.1 8.43 8.1 7.9 8.1 7.8 7.9 8 7.6 8.22 8.3 8.47 8.4 8.87 7.7 7.9 7.8 8.6 8.4 8.3 9 9.5 8.8 8.84 7.3 7.4 9.1 8.53 8.56 8.06 8.21 8.5 8.6 9.06 8.7 8.1 8.4 8.6 8.2 8.5 8.3 8 8.1 8.7 8.7 8.4 8.55 8.5 8.7 8.4 8.2 8.58 8.5 8.4 8.3 8.2 8 8.5 9.8 8 11.4 4.8 9 13.1 10.3 10.6 3.4 9.9 10.2 11.7 9.6 11.8 9.6 11.5 12 9.8 10.4 9.2 10.3 8.5 9.4 10.7 8.4 11.1 9.8 11.3 12.5 10.3 11.3 10.9 nan 8.9 10.5 5.5 7.1 7.7 8.8 13.4 12.5 12.1 12.6 9.6 11.21 10.2 7.9 11 9 10.8 11.8 10.8 9.5 10.8 11.3 10.4 nan 11.7 10.8 11.7 10.1 9.8 12.1 8.1 10.2 8.1 6.8 9.4 9.8 5.9 3.3 9.2 9.8 11.7 8.9 11.17 10.8 6 10.77 8.6 9.4 10.7 8.5 10.5 11.8 10.5 5.4 4.5 6.3 8.2 7.1 6.5 5.3 8.8 10 7.4 11.1 11.1 6 11.1 9.8 11.3 11.8 6 9.7 10.8 11.62 11.6 5.3 6.6 9.4 7.1 9 10.7 9.7 10.7 11.6 6.9 10 10.1 7.7 10.5 8.3 11.9 9.2 11.7 9.9 6.2 11.2 6.2 9.5 10.5 4.5 6.3 8.1 9.9 9 4.9 11 4.4 6 10.53 3.6 10.6 11.5 5.8 5.7 8.8 12.9 9.9 10.68 4.3 11.1 8.7 8.3 9.3 7.3 10.6 6.35 10.7 10.7 10.29 10.1 9.5 10.5 10 10.9 10.2 10.8 11.7 8.2 11 7.6 11.4 10.5 8.2 11.1 7.9 8.4 10.6 7 7.6 6.7 60.8 57.75 40.02 77.364 55.35 65.75 73.25 59.067 21.95 8 8 8.69 5 6.3 3 4.7 7 7 7 0.8 32.92 11.867 10.975 12.536 10.5 9 16 9 13 26 20.083 1.17 1.45 20.625 22.286 77 54.19 50 54.143 69.75 87 66.3 9 15 17.75 32.3 27.233 6.167 5.273 nan nan nan nan nan nan nan nan 4.083 4.575 4.326 2.933 3.275 3.136 38.125 34.037 136 129.375 35.75 29.5 27.4 26.76 11 11 10.4 13.5 12.146 31 53 36.248 125.6 173.75 94.405 53.333 70 63.51 56.717 61.05 57.75 101.875 85.982 63.625 82.111 65.333 58.331 49.625 47.778 47.229 41.5 40.167 32.056 5.889 7.25 7.838 53.425 57.848 0.222 1.549 5.83 74.667 131.4 45.273 42.636 48.429 11.818 10.556 12 31.091 28.333 30.125 10.936 10.078 11.088 391.5 130.67 39 35.66 37.6 39 49.9 51.113 8.3 10.207 79.077 81.333 41.25 40.226 46.167 47 41.163 53 127.833 100.83 50 19.22 26 nan 44 43 43.09 16 22.35 82.857 63.292 43.97 38.902 104.818 71.444 208.364 187.183 4.545 3.5 5.326 2.111 2.2 2.75 3.86 9.055 7.613 39.109 22.455 23.25 22.32 12.778 15.541 12.182 7.333 23.825 12.444 17.375 14.32 139.989 nan 82.852 6.238 1.288 5.33 2.302 10.416 9.248 1.535 4.99 0.886 1.39 1.527 1.588 1.223 1.47 1.448 1.32 1.42 1.443 1.718 0.642 2.942 1.715 1.51 3.976 1.572 0.63 0.73 0.23 3.33 3.78 3.02 0.735 0.81 3.414 4.071 6.096 3.829 8.543 7.83 4.555 4.87 4.535 0.23 3.02 3.07 4.508 1.651 1.172 0.91 3.245 2.22 2.55 1.32 2.64 4.17 5.97 nan 1.328 1.203 1.474 0.77 0.923 1.208 3.85 9.08 3.773 3.316 5.164 1.287 0.735 0.658 3.31 3.235 4.93 5.442 6.188 4.408 3.734 3.73 3.778 3.318 4.698 5.132 2.443 4.94 0.33 0.308 0.267 3.978 6.2 3.14 2.603 2.899 8.688 5.456 2.316 8.759 4.665 2.67 5.694 1.534 1.875 1.732 0.381 0.461 0.406 0.445 0.701 3.9 4.188 7.195 5.078 6.64 2.163 1.921 2.231 5.099 2.954 3.726 1.335 1.212 1.374 6.045 6.54 4.86 5.13 0.826 0.673 9.773 5.099 1.67 2.304 8.984 9.715 1.415 1.587 2.102 0.536 2.273 2.31 2.68 5.41 0.376 1.655 1.87 0.9 0.102 0.13 0.846 4.82 5.414 0.86 1.726 4.053 3.678 3.908 2.512 4.459 3.351 0.941 1.013 0.996 0.663 0.672 0.758 0.866 0.825 0.699 6.225 3.765 2.805 3.14 1.873 2.323 1.519 1.003 3.617 2.586 3.833 3.2 2.978 nan 2.8 578 370 346.667 98.182 233.7 430 110 205.667 102.75 5.8 21.571 18.429 27.286 8 46.2 14.75 34.333 31.333 49 81 42 208.333 12.5 58.5 28 20 20 120 60 60 49.5 13.5 10 228.75 178.57 122.85 647.57 76 51.429 37.5 22.5 39 10 40 35 192.5 28.333 18.333 33.636 10 5 10 20 10 10 10 nan 18 27.5 160 15 15 16.2 225 109 245 271.25 32.5 224.286 133.636 165 101 255 130 123.333 89.6 737.5 914 429.2 124.167 101.25 153 96.667 98.333 137 215.714 105.556 155 153.75 421.667 122.5 215.556 371.111 758.75 308.75 38.111 239 931.833 723.667 461.875 51.111 25 206.538 118.571 217.75 10 25.833 12.727 131.667 92 345.455 56.364 128.571 170.909 65.556 43.75 246.364 76.667 102.5 236 103.333 92.375 380 196 120 46.5 124 112.857 505 175 34 132.25 920 196.667 172.5 235 84.667 91.833 54.75 90 176.667 486.5 134 96.833 62.5 142 146.667 103.333 52.2 101.667 244.6 137.273 227.6 643 627.273 124.364 66.667 197.909 54.778 32.727 12.5 53.846 11.111 10 10.5 32 40 32.5 161.818 88.182 43.75 82.1 17.778 103 65.455 37.778 72.6 96.667 83.75 125.333 60.11 nan 27.069 105 428.75 125.667 61.182 58.222 18.25 61.25 44.667 36.3 27.25 12.75 10.667 12 16 13 4.25 18.667 20 41.5 15.6 16 3 3 9 4 4 26 12 72 246 53 1.625 2.5 196.62 182.42 143.71 59.429 264.9 276.85 10 27 16 5 27 13 12.75 7.3 7.75 9 1 1 1 1 2 1 2 nan 3.333 2 2.5 1.333 1.25 1.8 45 55 136.75 100 85.5 25.167 36 37.375 26.6 38.75 10.8 27.667 32 111.25 137.6 57.6 197.833 267.75 191.75 120.5 144.667 159.5 23 104.222 97.333 51.75 31.333 28.625 12.889 51.111 104.5 38.625 24.667 54 39 60.833 71 9.667 6.5 8.692 37.857 37 22.444 16.833 3.545 261.6 238.2 144 166.727 181 36.909 61.556 62.625 55 102.333 75.875 34.636 48.667 48.625 173 75 187 49 32.5 60 67.5 132.5 16.8 10.583 70 77.333 46.667 33.8 48 109 39 22.2 27.5 24 54.1 20.667 30.75 102 151.333 180.667 8.6 14.667 66.4 102.364 84.3 221.9 205.636 82.222 64.389 87.333 159.167 16 12.75 7.667 3.222 3.8 4 6 21.083 26.625 104.727 41.3 51.125 45.9 50.889 34.5 19.727 19.111 51.111 19.111 53.625 35.333 78.333 nan 64 170 558.75 187.057 138.7 97.58 56.667 111.75 77.434 71 46.6 20.75 19 17 15 61.6 98.25 50 57.833 61.5 18 40 27.5 11.5 44.136 13.6 nan 45 19 142 304 130.75 3 3 253.25 255.28 296 175.046 344.6 326.857 40.667 43.5 39 6 121 20.812 49.333 22.9 11.8 11.818 6.5 1 4 6 11 6 14 14 6.667 6.75 7.2 6 10.75 2.5 152.333 58.623 249.25 233.5 215.5 102.333 105.727 111.375 108 56.667 60 104 69.93 214 254.6 169.001 303.333 391.75 265.25 232.833 244 218 138.5 239 235.667 205.875 211.667 186.5 154.125 183.667 292.625 285.714 201.778 275.143 124.2 141.833 132.546 17.333 26 16.662 102.571 86.997 10.111 18.293 13.2 432.909 320.4 287 262.727 222.286 122 127.222 89.625 284 277.333 177.625 72.9 82.444 66.75 317 84 213 88.5 115 98.143 143.75 197.143 35.2 23.485 200.231 147.833 123.333 75.207 116.2 188.667 72.696 116.2 76.333 58.374 125.8 54.916 75.333 186 252.5 269.667 46.438 85 171.272 232.9 146.452 246.667 219.909 167.9 137.778 194.1 221.278 21.3 11 14.354 7 6.2 7.654 16 56.091 52.875 228.364 85.4 87.125 101.455 127 81.558 50.455 120.889 91.111 61.444 79.75 75.904 140.22 nan 140.517 50 1.3 15.6 1.4 10.5 28.4 3.2 6.9 5.544 0.8 0.8 0.6 41 0.5 0.3 1.1 1.1 0.4 0.8 0.5 7.6 1.7 1.5 3 0.5 2.7 0.8 0.5 4.9 2.8 5.8 0.2 0.3 12.32 8.957 3.7 13.2 22.5 11.84 3.9 3.3 0.8 1.1 2.8 12.1 7.9 4.5 0.5 0.8 nan nan nan nan nan nan nan nan nan 1 0.3 0.6 2.5 0.5 5.2 11.6 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