Mars weather data analysis using machine learning techniques
Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many publi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Priyadarshini, Ishaani [verfasserIn] Puri, Vikram [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Earth science informatics - Berlin : Springer, 2008, 14(2021), 4 vom: 04. Juli, Seite 1885-1898 |
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| Übergeordnetes Werk: |
volume:14 ; year:2021 ; number:4 ; day:04 ; month:07 ; pages:1885-1898 |
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| DOI / URN: |
10.1007/s12145-021-00643-0 |
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SPR045502056 |
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| 520 | |a Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. | ||
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| 650 | 4 | |a Gated Recurrent Units (GRU) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Long Short Term Memory (LSTM) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CNN-LSTM |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Puri, Vikram |e verfasserin |4 aut | |
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10.1007/s12145-021-00643-0 doi (DE-627)SPR045502056 (SPR)s12145-021-00643-0-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Priyadarshini, Ishaani verfasserin aut Mars weather data analysis using machine learning techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. Mars weather (dpeaa)DE-He213 Convolution Neural Networks (CNN) (dpeaa)DE-He213 Gated Recurrent Units (GRU) (dpeaa)DE-He213 Long Short Term Memory (LSTM) (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Puri, Vikram verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 14(2021), 4 vom: 04. Juli, Seite 1885-1898 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:14 year:2021 number:4 day:04 month:07 pages:1885-1898 https://dx.doi.org/10.1007/s12145-021-00643-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 04 07 1885-1898 |
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10.1007/s12145-021-00643-0 doi (DE-627)SPR045502056 (SPR)s12145-021-00643-0-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Priyadarshini, Ishaani verfasserin aut Mars weather data analysis using machine learning techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. Mars weather (dpeaa)DE-He213 Convolution Neural Networks (CNN) (dpeaa)DE-He213 Gated Recurrent Units (GRU) (dpeaa)DE-He213 Long Short Term Memory (LSTM) (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Puri, Vikram verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 14(2021), 4 vom: 04. Juli, Seite 1885-1898 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:14 year:2021 number:4 day:04 month:07 pages:1885-1898 https://dx.doi.org/10.1007/s12145-021-00643-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 04 07 1885-1898 |
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10.1007/s12145-021-00643-0 doi (DE-627)SPR045502056 (SPR)s12145-021-00643-0-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Priyadarshini, Ishaani verfasserin aut Mars weather data analysis using machine learning techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. Mars weather (dpeaa)DE-He213 Convolution Neural Networks (CNN) (dpeaa)DE-He213 Gated Recurrent Units (GRU) (dpeaa)DE-He213 Long Short Term Memory (LSTM) (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Puri, Vikram verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 14(2021), 4 vom: 04. Juli, Seite 1885-1898 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:14 year:2021 number:4 day:04 month:07 pages:1885-1898 https://dx.doi.org/10.1007/s12145-021-00643-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 04 07 1885-1898 |
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10.1007/s12145-021-00643-0 doi (DE-627)SPR045502056 (SPR)s12145-021-00643-0-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Priyadarshini, Ishaani verfasserin aut Mars weather data analysis using machine learning techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. Mars weather (dpeaa)DE-He213 Convolution Neural Networks (CNN) (dpeaa)DE-He213 Gated Recurrent Units (GRU) (dpeaa)DE-He213 Long Short Term Memory (LSTM) (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Puri, Vikram verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 14(2021), 4 vom: 04. Juli, Seite 1885-1898 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:14 year:2021 number:4 day:04 month:07 pages:1885-1898 https://dx.doi.org/10.1007/s12145-021-00643-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 04 07 1885-1898 |
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10.1007/s12145-021-00643-0 doi (DE-627)SPR045502056 (SPR)s12145-021-00643-0-e DE-627 ger DE-627 rakwb eng 550 004 ASE 550 ASE Priyadarshini, Ishaani verfasserin aut Mars weather data analysis using machine learning techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. Mars weather (dpeaa)DE-He213 Convolution Neural Networks (CNN) (dpeaa)DE-He213 Gated Recurrent Units (GRU) (dpeaa)DE-He213 Long Short Term Memory (LSTM) (dpeaa)DE-He213 CNN-LSTM (dpeaa)DE-He213 Puri, Vikram verfasserin aut Enthalten in Earth science informatics Berlin : Springer, 2008 14(2021), 4 vom: 04. Juli, Seite 1885-1898 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:14 year:2021 number:4 day:04 month:07 pages:1885-1898 https://dx.doi.org/10.1007/s12145-021-00643-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 04 07 1885-1898 |
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Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. 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Mars weather data analysis using machine learning techniques |
| abstract |
Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
| abstractGer |
Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
| abstract_unstemmed |
Abstract Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257. © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 |
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| container_issue |
4 |
| title_short |
Mars weather data analysis using machine learning techniques |
| url |
https://dx.doi.org/10.1007/s12145-021-00643-0 |
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| author2 |
Puri, Vikram |
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10.1007/s12145-021-00643-0 |
| up_date |
2024-07-03T16:24:58.179Z |
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| score |
7.3998346 |