{
    "data_id": "42701",
    "name": "Methane",
    "exact_name": "Methane",
    "version": 1,
    "version_label": null,
    "description": "**Author**: Marek Sikora, Lukasz Wrobel  \r\n**Source**: coal mine in Poland - March 2, 2014 - June 16, 2014  \r\n**Please cite**: Slezak, D., Grzegorowski, M., Janusz, A., Kozielski, M., Nguyen, S. H., Sikora, M., Stawicki, S. & Wrobel, L. (2018). A framework for learning and embedding multi-sensor forecasting models into a decision support system: A case study of methane concentration in coal mines. Information Sciences, 451, 112-133.  \r\n\r\nCoal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions or rock-burst. To provide protection for people working underground, systems for active monitoring of a production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) in order to prevent spontaneous explosions. Therefore, for that purpose the ability to predict dangerous concentrations of gases in the nearest future can be even more important than monitoring the current sensor readings.\r\n\r\nThe data set contains raw data collected at an underground coal mine. It consists of a data stamp and measurements collected each second. The considered task related to this data set is to construct a model capable of predicting dangerous concentrations of methane at longwalls of a coal mine in a chosen time horizon,\r\nTherefore, in case of classification task the model has to predict weather the methane concentration for three methane meters: MM263, MM264 and MM256 exceeds the chosen threshold (e.g. 1.0) within the chosen period of time (e.g. three to six minutes). In case of regression task it is required to predict the value of methane concentration for the selected methane meters.\r\n\r\n### Attribute Information:\r\nAN311 - anemometer (distant) [m\/s]\r\n\r\n* sensor type: anemometer -5-5\r\n* kind: alarming\r\n\r\nAN422 - anemometer [m\/s]\r\n\r\n* sensor type: anemometer -5-5\r\n* kind: switching off\r\n\r\nAN423 - anemometer [m\/s]\r\n\r\n* sensor type: anemometer -5-5\r\n* kind: switching off\r\n\r\nTP1721 - temperature [C]\r\n\r\n* sensor type: temperature THP (three-component sensor THP2\/93)\r\n* kind: registering\r\n\r\nRH1722 - humidity [%RH]\r\n\r\n* sensor type: humidity THP (three-component sensor THP2\/93)\r\n* kind: registering\r\n\r\nBA1723 - barometer [hPa]\r\n\r\n* sensor type: barometer THP (three-component sensor THP2\/93)\r\n* kind: registering\r\n\r\nTP1711 - temperature [C]\r\n\r\n* sensor type: temperature THP (three-component sensor THP2\/94)\r\n* kind: registering\r\n\r\nRH1712 - humidity [%RH]\r\n\r\n* sensor type: humidity THP (three-component sensor THP2\/94)\r\n* kind: registering\r\n\r\nBA1713 - barometer [hPa]\r\n\r\n* sensor type: barometer THP (three-component sensor THP2\/94)\r\n* kind: registering\r\n\r\nMM252 – methane meter (distant) [%CH4]\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A (alarm): 2.0%\r\n* value of threshold W (warning): 1.5%\r\n\r\nMM261 – methane meter [%CH4]\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A: 1.5%\r\n* value of threshold W: 1.0%\r\n\r\nMM262 - methane meter [%CH4]\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A: 1.0%\r\n* value of threshold W: 0.6%\r\n\r\nMM263 - methane meter [%CH4] - !target sensor!\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A: 1.5%\r\n* value of threshold W: 1.0%\r\n\r\nMM264 - methane meter [%CH4] - !target sensor!\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A: 1.5%\r\n* value of threshold W: 1.0%\r\n\r\nMM256 - methane meter [%CH4] - !target sensor!\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A: 1.5%\r\n* value of threshold W: 1.0%\r\n\r\nMM211 - methane meter [%CH4]\r\n\r\n* sensor type: methane meter MM-2PWk\r\n* kind: switching off\r\n* value of threshold A: 2.0%\r\n* value of threshold W: 1.5%\r\n\r\nCM861 – high concentration methane meter [%CH4]\r\n\r\n* sensor type: methane meter (0…100)\r\n* kind: registering\r\n\r\nCR863 – sensor for pressure difference on the methane drainage flange [Pa]\r\n\r\n* sensor type: pressure difference (0..250)\r\n* kind: registering\r\n\r\nCR863 - sensor for pressure difference on the methane drainage flange [Pa]\r\n\r\n* sensor type: pressure difference (0..250)\r\n* kind: registering\r\n\r\nP_864 – pressure inside the methane drainage pipeline [kPa]\r\n\r\n* sensor type: pressure (0..110)\r\n* kind: registering\r\n\r\nTC862 – temperature inside the pipeline [C]\r\n\r\n* sensor type: temperature (10..40)\r\n* kind: registering\r\n\r\nWM868 – methane delivery calculated according to CM, CR, P, TC [m3\/mi]\r\n\r\n* sensor type: methane delivery (0..50)\r\n* kind: registering\r\n\r\nAMP1_IR - current of the left cutting head of the cutter loader [A]\r\n\r\nAMP2_IR - current of the right cutting head of the cutter loader [A]\r\n\r\nDMP3_IR - current of the left haulage in the cutter loader [A]\r\n\r\nDMP4_IR - current of the right haulage in the cutter loader [A]\r\n\r\nAMP5_IR – current of the hydraulic pump engine in the cutter loader [A]\r\n\r\nF_SIDE - driving direction, 1=left, {0, 0.5}=right\r\n\r\nV - cutter loader speed [Hz] (Vmin=3Hz, Vmax=100Hz. Herz values are then transformed into m\/min - 100Hz equal to about 20 m\/min)\r\n",
    "format": "ARFF",
    "uploader": "Michal Kozielski",
    "uploader_id": 671,
    "visibility": "public",
    "creator": "\"Marek Sikora\",\"Lukasz Wrobel\"",
    "contributor": null,
    "date": "2020-10-02 16:15:13",
    "update_comment": null,
    "last_update": "2020-10-02 16:15:13",
    "licence": "CC0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22044589\/methane_data.arff",
    "default_target_attribute": "MM263,MM264,MM256",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
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            "Coal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions or rock-burst. To provide protection for people working underground, systems for active monitoring of a production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) in order to prevent spontaneous explosions. Therefore, for that purpose the ability to predict dangerou "
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        "weight": 5
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    "qualities": {
        "NumberOfInstances": 9199930,
        "NumberOfFeatures": 34,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 33,
        "NumberOfSymbolicFeatures": 1,
        "Dimensionality": 3.6956802932196225e-6,
        "PercentageOfNumericFeatures": 97.05882352941177,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 2.941176470588235,
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        "NumberOfBinaryFeatures": 0,
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        "AutoCorrelation": null,
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Language"
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        {
            "uploader": "38960",
            "tag": "Machine Learning"
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    "features": [
        {
            "name": "MM263",
            "index": "18",
            "type": "numeric",
            "distinct": "41",
            "missing": "0",
            "target": "1",
            "min": "-2",
            "max": "30",
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            "name": "MM264",
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        {
            "name": "MM256",
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            "name": "year",
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            "type": "numeric",
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            "min": "2014",
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            "mean": "2014",
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        },
        {
            "name": "month",
            "index": "1",
            "type": "numeric",
            "distinct": "4",
            "missing": "0",
            "min": "3",
            "max": "6",
            "mean": "4",
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            "name": "day",
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            "type": "numeric",
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            "mean": "15",
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            "name": "hour",
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            "mean": "11",
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        },
        {
            "name": "minute",
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            "type": "numeric",
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            "name": "second",
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            "mean": "3",
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            "name": "RH1712",
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            "missing": "0",
            "min": "0",
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            "name": "BA1713",
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            "name": "MM252",
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            "name": "MM261",
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            "name": "MM262",
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            "min": "0",
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            "mean": "0",
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            "name": "MM211",
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            "mean": "1",
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            "name": "CM861",
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