'연구 노트' 카테고리의 글 목록 (2 Page)

연구 노트

TKE_pycar_Lv3_0m-결과-241002-0_RH (Year, Min, RH 켜기 Rain 고려 안함) 결과 R2<0.8 2024.10.02
TKE_pycar_Lv3_300m-결과-241002-300.ipynb (300m Lv3 rain 처리 Year/Min 제거 R2 > 0.8 ) 2024.10.02
TKE_pycar_Lv3_60m-결과-241002-60.ipynb (60m Lv3 rain 처리 Year/Min 제거 R2 > 0.8 ) 2024.10.02
TKE_pycar_Lv3_0m-결과.ipynb (0m Lv3 rain무처리 R2 > 0.8 ) 2024.10.02
TKE_pycar_Lv3_60m-결과-241002-60.ipynb (60m Lv3 rain 처리 R2 > 0.8 ) 2024.10.02
140m lv3 2024.09.27
Ubuntu :: XRDP 2024.09.12
TKE 예측 결과 2024.08.14
ModuleNotFoundError: No module named 'sklearn.neighbors.base' 2024.05.24
ModuleNotFoundError: No module named 'h2o' 2024.05.24

TKE_pycar_Lv3_0m-결과-241002-0_RH (Year, Min, RH 켜기 Rain 고려 안함) 결과 R2<0.8

airmaster 2024. 10. 2. 18:25

2024. 10. 2. 18:25

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rain 고려 안함.

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저작자표시 비영리 변경금지

TKE_pycar_Lv3_300m-결과-241002-300.ipynb (300m Lv3 rain 처리 Year/Min 제거 R2 > 0.8 )

airmaster 2024. 10. 2. 14:18

2024. 10. 2. 14:18

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저작자표시 비영리 변경금지

TKE_pycar_Lv3_60m-결과-241002-60.ipynb (60m Lv3 rain 처리 Year/Min 제거 R2 > 0.8 )

airmaster 2024. 10. 2. 12:51

2024. 10. 2. 12:51

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Year/Min 제거 시 0.8108 -->0.8123

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저작자표시 비영리 변경금지

TKE_pycar_Lv3_0m-결과.ipynb (0m Lv3 rain무처리 R2 > 0.8 )

airmaster 2024. 10. 2. 11:03

2024. 10. 2. 11:03

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저작자표시 비영리 변경금지

TKE_pycar_Lv3_60m-결과-241002-60.ipynb (60m Lv3 rain 처리 R2 > 0.8 )

airmaster 2024. 10. 2. 10:53

2024. 10. 2. 10:53

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TKE_pycar_Lv3_60m-결과-241002-60.ipynb

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저작자표시 비영리 변경금지

140m lv3

airmaster 2024. 9. 27. 11:34

2024. 9. 27. 11:34

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inp = tmp[['Datetime', #'doy',
       #   'Year', 'Month', 'Day', 'Hour', 'Min',# 'Week',  # Year Min 제거하면 R2 내려감.
       #'Solar_time_hr', 'Solar_rad_MJ_m2', 'Netrad_accum_MJ_m2',
       # 'Insolation_accum_MJ_m2', 'NetRs_Avg',
       # 'Soil.moist_10cm',
      'T_soil_20cm',  'H_soil_20cm', 'Wtr_soil_20cm',
      'Rain_mm', # 'Vis_10m', # 'P_vapor',
       'Td', 'T_sfc', #'T_gnd_5cm', 'T_gnd_10cm',#'T_gnd_20cm','T_gnd_30cm','T_gnd_0.5m',
  #     'T_0.5m', #'T_1.5m',
       'Ts_1.5m', 'Ts_60m', 'Ts_140m', 'Ts_300m',
     #  'RH_0.5m', 'RH_1.5m',

       'WS_1.5m', 'WS_60m', 'WS_140m', 'WS_300m',
       'WD_1.5m', 'WD_60m', 'WD_140m', 'WD_300m',

       'dudz_300m', 'dudz_300m1', 'dudz_300m2',
        'dudz_140m', 'dudz_140m1','dudz_60m',
       'dTdz_300m', 'dTdz_300m1', 'dTdz_300m2',
        'dTdz_140m', 'dTdz_140m1','dTdz_60m',
        'dTdz_1.5m', 'dTdz_sfc',

       'dudt_300m', 'dudt_140m', 'dudt_60m',
       'dTdt_300m', 'dTdt_140m', 'dTdt_60m', 'dTdt_1.5m', 'dTdt_sfc',


       'Kdn', 'Kup', 'Ldn', 'Lup',
           'netrad',
# 'Ri_300m', 'Ri_300m1', 'Ri_300m2', 'Ri_140m', 'Ri_140m1', 'Ri_60m',   #R2 이 내려감

        'TKE'+lev
   # 'TKE_60m' ,'TKE'+lev ,'TKE_300m'
        ]]

Model MAE MSE RMSE R2 RMSLE MAPE0

Stacking Regressor

0.3504

0.4375

0.6615

0.7771

0.1939

0.4632

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5
[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0

Model MAE MSE RMSE R2 RMSLE MAPE0

Voting Regressor

0.3509

0.4408

0.6639

0.7755

0.1938

0.5135

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5
[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0

inp = tmp[['Datetime', #'doy',
          #'Year',
           'Month', 'Day', 'Hour', #'Min',# 'Week',  # Year Min 제거하면 R2 내려감.
       #'Solar_time_hr', 'Solar_rad_MJ_m2', 'Netrad_accum_MJ_m2',
       # 'Insolation_accum_MJ_m2', 'NetRs_Avg',
       # 'Soil.moist_10cm',
      'T_soil_20cm',  'H_soil_20cm', 'Wtr_soil_20cm',
       'Rain_mm', #'Vis_10m', 'P_vapor',
       'Td', 'T_sfc', 'T_gnd_5cm', #'T_gnd_10cm','T_gnd_20cm','T_gnd_30cm','T_gnd_0.5m',
       'T_0.5m', 'T_1.5m',
       'Ts_1.5m', 'Ts_60m', 'Ts_140m', 'Ts_300m',
     #  'RH_0.5m', 'RH_1.5m',

       'WS_1.5m', 'WS_60m', 'WS_140m', 'WS_300m',
       'WD_1.5m', 'WD_60m', 'WD_140m', 'WD_300m',

       'dudz_300m', 'dudz_300m1', 'dudz_300m2',
        'dudz_140m', 'dudz_140m1','dudz_60m',
       'dTdz_300m', 'dTdz_300m1', 'dTdz_300m2',
        'dTdz_140m', 'dTdz_140m1','dTdz_60m',
        'dTdz_1.5m', 'dTdz_sfc',

       'dudt_300m', 'dudt_140m', 'dudt_60m',
       'dTdt_300m', 'dTdt_140m', 'dTdt_60m', 'dTdt_1.5m', 'dTdt_sfc',


       'Kdn', 'Kup', 'Ldn', 'Lup',
           'netrad',
# 'Ri_300m', 'Ri_300m1', 'Ri_300m2', 'Ri_140m', 'Ri_140m1', 'Ri_60m',   #R2 이 내려감

        'TKE'+lev
   # 'TKE_60m' ,'TKE'+lev ,'TKE_300m'
        ]]

Model MAE MSE RMSE R2 RMSLE MAPE0

Stacking Regressor

0.3775

0.6732

0.8205

0.7455

0.2081

0.5207

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5
[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0

Voting Regressor

0.3793

0.6844

0.8273

0.7413

0.2083

0.5883

Model MAE MSE RMSE R2 RMSLE MAPE0

RH 제거 전

날짜 시간 제거 전

Stacking Regressor

0.3771

0.6644

0.8151

0.7489

0.2067

0.5160

Model MAE MSE RMSE R2 RMSLE MAPE0

Stacking Regressor

0.3771

0.6644

0.8151

0.7489

0.2067

0.5160

[LightGBM] [Warning] bagging_freq is set=4, subsample_freq=0 will be ignored. Current value: bagging_freq=4
[LightGBM] [Warning] feature_fraction is set=0.5, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5
[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0

Voting Regressor

0.3776

0.6775

0.8231

0.7439

0.2063

0.5828

Model MAE MSE RMSE R2 RMSLE MAPE0

Extra Trees Regressor

0.3897

0.7530

0.8678

0.7154

0.2123

0.6284

Model MAE MSE RMSE R2 RMSLE MAPE0

Extreme Gradient Boosting

0.3947

0.6855

0.8280

0.7409

0.2173

0.5931

Light Gradient Boosting Machine

0.4024

0.7101

0.8427

0.7316

0.2201

0.6230

Model MAE MSE RMSE R2 RMSLE MAPE0

inp = tmp[['Datetime', #'doy',
          'Year', 'Month', 'Day', 'Hour', 'Min',# 'Week',  # Year Min 제거하면 R2 내려감.
       #'Solar_time_hr', 'Solar_rad_MJ_m2', 'Netrad_accum_MJ_m2',
       # 'Insolation_accum_MJ_m2', 'NetRs_Avg',
       # 'Soil.moist_10cm',
      'T_soil_20cm',  'H_soil_20cm', 'Wtr_soil_20cm',
    #   'Rain_mm', 'Vis_10m', 'P_vapor',
       'Td', 'T_sfc', 'T_gnd_5cm', #'T_gnd_10cm','T_gnd_20cm','T_gnd_30cm','T_gnd_0.5m',
       'T_0.5m', 'T_1.5m',
       'Ts_1.5m', 'Ts_60m', 'Ts_140m', 'Ts_300m',
       'RH_0.5m', 'RH_1.5m',

       'WS_1.5m', 'WS_60m', 'WS_140m', 'WS_300m',
       'WD_1.5m', 'WD_60m', 'WD_140m', 'WD_300m',

       'dudz_300m', 'dudz_300m1', 'dudz_300m2',
        'dudz_140m', 'dudz_140m1','dudz_60m',
       'dTdz_300m', 'dTdz_300m1', 'dTdz_300m2',
        'dTdz_140m', 'dTdz_140m1','dTdz_60m',
        'dTdz_1.5m', 'dTdz_sfc',

       'dudt_300m', 'dudt_140m', 'dudt_60m',
       'dTdt_300m', 'dTdt_140m', 'dTdt_60m', 'dTdt_1.5m', 'dTdt_sfc',


       'Kdn', 'Kup', 'Ldn', 'Lup',
           'netrad',
# 'Ri_300m', 'Ri_300m1', 'Ri_300m2', 'Ri_140m', 'Ri_140m1', 'Ri_60m',   #R2 이 내려감

        'TKE'+lev
   # 'TKE_60m' ,'TKE'+lev ,'TKE_300m'
        ]]

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저작자표시 비영리 변경금지

Ubuntu :: XRDP

airmaster 2024. 9. 12. 18:04

2024. 9. 12. 18:04

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You have to be logged out of your account on ubuntu desktop. Else RDP on windows will keep quitting.

You need to Export follwing enviorment variables

export GNOME_SHELL_SESSION_MODE=ubuntu
export XDG_CURRENT_DESKTOP=ubuntu:GNOME

Either do sudo nano .xsessionrc or sudo nano /etc/xrdp/startwm.sh and place the above two lines at the very start and reboot your pc.

Remote Desktop from Windows onto Ubuntu 22.04 takes me to a XRDP login then a blank screen - Ask Ubuntu

Remote Desktop from Windows onto Ubuntu 22.04 takes me to a XRDP login then a blank screen

I'm trying to get the Remote Desktop feature working on Ubuntu (Desktop) 22.04, but I can't seem to connect to my Ubuntu desktop from a Windows (10) PC. I'm using these instructions: https://help....

askubuntu.com

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저작자표시 비영리 변경금지

TKE 예측 결과

airmaster 2024. 8. 14. 12:18

2024. 8. 14. 12:18

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T_soil, H_soil, Wtr_soil 없느 ㄴ경우가 R2가 높다

<class 'pandas.core.frame.DataFrame'>
Index: 62976 entries, 14449 to 105215
Data columns (total 35 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Month       62976 non-null  int64  
 1   Day         62976 non-null  int64  
 2   Hour        62976 non-null  int64  
 3   Min         62976 non-null  int64  
 4   Rain_mm     62976 non-null  float64
 5   Vis_10m     62976 non-null  float64
 6   P_vapor     62976 non-null  float64
 7   Td          62976 non-null  float64
 8   T_sfc       62976 non-null  float64
 9   T_gnd_5cm   62976 non-null  float64
 10  T_gnd_10cm  62976 non-null  float64
 11  T_gnd_20cm  62976 non-null  float64
 12  T_gnd_30cm  62976 non-null  float64
 13  T_gnd_0.5m  62976 non-null  float64
 14  T_0.5m      62976 non-null  float64
 15  T_1.5m      62976 non-null  float64
 16  Ts_1.5m     62976 non-null  float64
 17  Ts_60m      62976 non-null  float64
 18  Ts_140m     62976 non-null  float64
 19  Ts_300m     62976 non-null  float64
 20  RH_0.5m     62976 non-null  float64
 21  RH_1.5m     62976 non-null  float64
 22  WS_1.5m     62976 non-null  float64
 23  WS_60m      62976 non-null  float64
 24  WS_140m     62976 non-null  float64
 25  WS_300m     62976 non-null  float64
 26  WD_1.5m     62976 non-null  float64
 27  WD_60m      62976 non-null  float64
 28  WD_140m     62976 non-null  float64
 29  WD_300m     62976 non-null  float64
 30  Kdn         62976 non-null  float64
 31  Kup         62976 non-null  float64
 32  Ldn         62976 non-null  float64
 33  Lup         62976 non-null  float64
 34  TKE_0m      62976 non-null  float64
dtypes: float64(31), int64(4)

met data 내삽 완료 except winds

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 105216 entries, 0 to 105215
Data columns (total 39 columns):
 #   Column         Non-Null Count   Dtype  
---  ------         --------------   -----  
 0   Datetime       105216 non-null  object 
 1   Month          105216 non-null  int64  
 2   Day            105216 non-null  int64  
 3   Hour           105216 non-null  int64  
 4   Min            105216 non-null  int64  
 5   T_soil_20cm    105216 non-null  float64
 6   H_soil_20cm    105216 non-null  float64
 7   Wtr_soil_20cm  105216 non-null  float64
 8   Rain_mm        105216 non-null  float64
 9   Vis_10m        93482 non-null   float64
 10  P_vapor        105216 non-null  float64
 11  Td             105216 non-null  float64
 12  T_sfc          105216 non-null  float64
 13  T_gnd_5cm      105216 non-null  float64
 14  T_gnd_10cm     105216 non-null  float64
 15  T_gnd_20cm     105216 non-null  float64
 16  T_gnd_30cm     105216 non-null  float64
 17  T_gnd_0.5m     105216 non-null  float64
 18  T_0.5m         105024 non-null  float64
 19  T_1.5m         105216 non-null  float64
 20  Ts_1.5m        105216 non-null  float64
 21  Ts_60m         105216 non-null  float64
 22  Ts_140m        105216 non-null  float64
 23  Ts_300m        105216 non-null  float64
 24  RH_0.5m        105216 non-null  float64
 25  RH_1.5m        105216 non-null  float64
 26  WS_1.5m        105214 non-null  float64
 27  WS_60m         84668 non-null   float64
 28  WS_140m        97149 non-null   float64
 29  WS_300m        92835 non-null   float64
 30  WD_1.5m        105214 non-null  float64
 31  WD_60m         83083 non-null   float64
 32  WD_140m        80702 non-null   float64
 33  WD_300m        92331 non-null   float64
 34  Kdn            105075 non-null  float64
 35  Kup            104997 non-null  float64
 36  Ldn            105075 non-null  float64
 37  Lup            105075 non-null  float64
 38  TKE_0m         87025 non-null   float64
dtypes: float64(34), int64(4), object(1)
memory usage: 31.3+ MB

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ModuleNotFoundError: No module named 'sklearn.neighbors.base'

airmaster 2024. 5. 24. 10:53

2024. 5. 24. 10:53

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>>> from missingpy import KNNImputer

에러: ModuleNotFoundError: No module named 'sklearn.neighbors.base'

아래와 같이 변경할 것.

>>>from sklearn.impute import KNNImputer

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ModuleNotFoundError: No module named 'h2o'

airmaster 2024. 5. 24. 10:41

2024. 5. 24. 10:41

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보통 모듈 설치를 위해 아래 명령을 사용하는데,

>>> conda install h2o

하지만, 아래와 같이 pythyo 이 읽어들이질 못한다.

ModuleNotFoundError: No module named 'h2o'
ModuleNotFoundError: No module named 'missingpy'

이때는 pip install 사용해 주면 성공.

>>> pip install h2o

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기상 기후 강의 노트

연구 노트

TKE_pycar_Lv3_0m-결과-241002-0_RH (Year, Min, RH 켜기 Rain 고려 안함) 결과 R2<0.8

TKE_pycar_Lv3_300m-결과-241002-300.ipynb (300m Lv3 rain 처리 Year/Min 제거 R2 > 0.8 )

TKE_pycar_Lv3_60m-결과-241002-60.ipynb (60m Lv3 rain 처리 Year/Min 제거 R2 > 0.8 )

TKE_pycar_Lv3_0m-결과.ipynb (0m Lv3 rain무처리 R2 > 0.8 )

TKE_pycar_Lv3_60m-결과-241002-60.ipynb (60m Lv3 rain 처리 R2 > 0.8 )

140m lv3

Ubuntu :: XRDP

TKE 예측 결과

ModuleNotFoundError: No module named 'sklearn.neighbors.base'

ModuleNotFoundError: No module named 'h2o'

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