├── README.md
└── XgBoost_Ensemble.ipynb
/README.md:
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1 | # Landslide-Forecasting-System
2 |
3 | ● Its just a sample of models which i have implemneted not actual optimised model. Actual codes are in the link https://github.com/ambuj501/Time-Series-Forcasting
4 |
5 | ● In this code i have used practical geographical data of landslide which include various factors that affact the landslide like temprature, moisture, rain, pressure, forces, acceleration, velocity of rocks/lands and displacement in x,y,z directions.
6 |
7 | ● This model predict the overall displacement of rock/land which can further classify into different alert zone like Red alert, Green alert and Yellow alert.
8 |
9 |
10 |
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/XgBoost_Ensemble.ipynb:
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1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "name": "XGBoost_A.ipynb",
7 | "provenance": []
8 | },
9 | "kernelspec": {
10 | "name": "python3",
11 | "display_name": "Python 3"
12 | }
13 | },
14 | "cells": [
15 | {
16 | "cell_type": "code",
17 | "metadata": {
18 | "id": "qRkbJaI8XLVq",
19 | "colab_type": "code",
20 | "colab": {}
21 | },
22 | "source": [
23 | "rimport numpy as np\n",
24 | "import pandas as pd\n",
25 | "from xgboost import plot_importance\n"
26 | ],
27 | "execution_count": 0,
28 | "outputs": []
29 | },
30 | {
31 | "cell_type": "code",
32 | "metadata": {
33 | "id": "5YppiBg0YjXh",
34 | "colab_type": "code",
35 | "outputId": "995b839d-82cc-476e-b9cd-44aa915ffd8a",
36 | "colab": {
37 | "base_uri": "https://localhost:8080/",
38 | "height": 206
39 | }
40 | },
41 | "source": [
42 | "data = pd.read_csv('Data_A.csv')\n",
43 | "data.head()"
44 | ],
45 | "execution_count": 0,
46 | "outputs": [
47 | {
48 | "output_type": "execute_result",
49 | "data": {
50 | "text/html": [
51 | "
\n",
52 | "\n",
65 | "
\n",
66 | " \n",
67 | " \n",
68 | " | \n",
69 | " MLP | \n",
70 | " CNN | \n",
71 | " LSTM | \n",
72 | " ConvLSTM | \n",
73 | " CNNLSTM | \n",
74 | " Y_Actual | \n",
75 | "
\n",
76 | " \n",
77 | " \n",
78 | " \n",
79 | " | 0 | \n",
80 | " 745.45450 | \n",
81 | " 578.076660 | \n",
82 | " 593.918884 | \n",
83 | " 601.86250 | \n",
84 | " 603.209412 | \n",
85 | " 753.823965 | \n",
86 | "
\n",
87 | " \n",
88 | " | 1 | \n",
89 | " 354.08030 | \n",
90 | " 418.707611 | \n",
91 | " 404.187592 | \n",
92 | " 406.27112 | \n",
93 | " 403.875183 | \n",
94 | " 325.702181 | \n",
95 | "
\n",
96 | " \n",
97 | " | 2 | \n",
98 | " 369.19270 | \n",
99 | " 404.535706 | \n",
100 | " 413.250580 | \n",
101 | " 411.56372 | \n",
102 | " 405.864929 | \n",
103 | " 381.832205 | \n",
104 | "
\n",
105 | " \n",
106 | " | 3 | \n",
107 | " 436.41922 | \n",
108 | " 434.756683 | \n",
109 | " 435.081787 | \n",
110 | " 444.64954 | \n",
111 | " 441.274383 | \n",
112 | " 443.038006 | \n",
113 | "
\n",
114 | " \n",
115 | " | 4 | \n",
116 | " 516.93665 | \n",
117 | " 474.673157 | \n",
118 | " 514.759522 | \n",
119 | " 522.50120 | \n",
120 | " 508.574493 | \n",
121 | " 503.232639 | \n",
122 | "
\n",
123 | " \n",
124 | "
\n",
125 | "
"
126 | ],
127 | "text/plain": [
128 | " MLP CNN LSTM ConvLSTM CNNLSTM Y_Actual\n",
129 | "0 745.45450 578.076660 593.918884 601.86250 603.209412 753.823965\n",
130 | "1 354.08030 418.707611 404.187592 406.27112 403.875183 325.702181\n",
131 | "2 369.19270 404.535706 413.250580 411.56372 405.864929 381.832205\n",
132 | "3 436.41922 434.756683 435.081787 444.64954 441.274383 443.038006\n",
133 | "4 516.93665 474.673157 514.759522 522.50120 508.574493 503.232639"
134 | ]
135 | },
136 | "metadata": {
137 | "tags": []
138 | },
139 | "execution_count": 47
140 | }
141 | ]
142 | },
143 | {
144 | "cell_type": "code",
145 | "metadata": {
146 | "id": "5elnv2FYYoZq",
147 | "colab_type": "code",
148 | "colab": {}
149 | },
150 | "source": [
151 | "\n",
152 | "mn = data.min()\n",
153 | "mx = data.max()\n",
154 | "data = (data-mn)/(mx-mn)"
155 | ],
156 | "execution_count": 0,
157 | "outputs": []
158 | },
159 | {
160 | "cell_type": "code",
161 | "metadata": {
162 | "id": "WW1b9kT4YyOf",
163 | "colab_type": "code",
164 | "colab": {}
165 | },
166 | "source": [
167 | "y = data.iloc[:,-1].values\n",
168 | "x = data.iloc[:,:-1].values\n",
169 | "x_train = x[:1304]\n",
170 | "y_train = y[:1304]\n",
171 | "x_test = x[1304:]\n",
172 | "y_test = y[1304:]"
173 | ],
174 | "execution_count": 0,
175 | "outputs": []
176 | },
177 | {
178 | "cell_type": "code",
179 | "metadata": {
180 | "id": "WMXrBehIZOfI",
181 | "colab_type": "code",
182 | "outputId": "eafddbc3-7be2-4cd1-8ecc-617554b49473",
183 | "colab": {
184 | "base_uri": "https://localhost:8080/",
185 | "height": 90
186 | }
187 | },
188 | "source": [
189 | "print(x_train.shape)\n",
190 | "print(y_train.shape)\n",
191 | "print(x_test.shape)\n",
192 | "print(y_test.shape)"
193 | ],
194 | "execution_count": 0,
195 | "outputs": [
196 | {
197 | "output_type": "stream",
198 | "text": [
199 | "(1304, 5)\n",
200 | "(1304,)\n",
201 | "(259, 5)\n",
202 | "(259,)\n"
203 | ],
204 | "name": "stdout"
205 | }
206 | ]
207 | },
208 | {
209 | "cell_type": "code",
210 | "metadata": {
211 | "id": "Su1Faiw0ZSav",
212 | "colab_type": "code",
213 | "outputId": "0dc87c63-3c1d-4880-ea37-cff06b4f7c1a",
214 | "colab": {
215 | "base_uri": "https://localhost:8080/",
216 | "height": 182
217 | }
218 | },
219 | "source": [
220 | "import xgboost as xgb\n",
221 | "model=xgb.XGBRegressor(base_score=1, booster='gbtree', colsample_bylevel=1,\n",
222 | " colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
223 | " importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
224 | " max_depth=2, min_child_weight=1, missing=None, n_estimators=100,\n",
225 | " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
226 | " reg_alpha=10, reg_lambda=5, scale_pos_weight=1, seed=None,\n",
227 | " silent=None, subsample=1, verbosity=1)\n",
228 | "\n",
229 | "model.fit(x_train, y_train)\n",
230 | "\n",
231 | "\n"
232 | ],
233 | "execution_count": 0,
234 | "outputs": [
235 | {
236 | "output_type": "stream",
237 | "text": [
238 | "[16:06:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
239 | ],
240 | "name": "stdout"
241 | },
242 | {
243 | "output_type": "execute_result",
244 | "data": {
245 | "text/plain": [
246 | "XGBRegressor(base_score=5, booster='gbtree', colsample_bylevel=1,\n",
247 | " colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
248 | " importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
249 | " max_depth=2, min_child_weight=1, missing=None, n_estimators=100,\n",
250 | " n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
251 | " reg_alpha=10, reg_lambda=5, scale_pos_weight=1, seed=None,\n",
252 | " silent=None, subsample=1, verbosity=1)"
253 | ]
254 | },
255 | "metadata": {
256 | "tags": []
257 | },
258 | "execution_count": 84
259 | }
260 | ]
261 | },
262 | {
263 | "cell_type": "code",
264 | "metadata": {
265 | "id": "ekH6SkeCZhdA",
266 | "colab_type": "code",
267 | "colab": {}
268 | },
269 | "source": [
270 | "y_pred = model.predict(x)\n",
271 | "a_scale = (mx[5]-mn[5])*(y)+ mn[5]\n",
272 | "p_scale = (mx[5]-mn[5])*(y_pred)+ mn[5]"
273 | ],
274 | "execution_count": 0,
275 | "outputs": []
276 | },
277 | {
278 | "cell_type": "code",
279 | "metadata": {
280 | "id": "hLNlFhyNZ7ze",
281 | "colab_type": "code",
282 | "outputId": "05a7fb4e-35ac-4132-c30f-eb9590003057",
283 | "colab": {
284 | "base_uri": "https://localhost:8080/",
285 | "height": 53
286 | }
287 | },
288 | "source": [
289 | "print(a_scale.shape)\n",
290 | "print(p_scale.shape)"
291 | ],
292 | "execution_count": 0,
293 | "outputs": [
294 | {
295 | "output_type": "stream",
296 | "text": [
297 | "(1563,)\n",
298 | "(1563,)\n"
299 | ],
300 | "name": "stdout"
301 | }
302 | ]
303 | },
304 | {
305 | "cell_type": "code",
306 | "metadata": {
307 | "id": "VGD9dd8naHAV",
308 | "colab_type": "code",
309 | "colab": {}
310 | },
311 | "source": [
312 | "test_a =np.array(a_scale[-259:])\n",
313 | "test_p = np.array(p_scale[-259:])\n",
314 | "train_a = np.array(a_scale[:-259])\n",
315 | "train_p = np.array(p_scale[:-259])"
316 | ],
317 | "execution_count": 0,
318 | "outputs": []
319 | },
320 | {
321 | "cell_type": "code",
322 | "metadata": {
323 | "id": "V4n3mXXbaNRt",
324 | "colab_type": "code",
325 | "colab": {}
326 | },
327 | "source": [
328 | "test_a_10 = list()\n",
329 | "test_p_10 = list()\n",
330 | "for i in range(0, test_a.shape[0], 7):\n",
331 | " test_a_10.append(np.sum(test_a[i:i+7]))\n",
332 | " test_p_10.append(np.sum(test_p[i:i+7]))\n",
333 | "test_a_10 = np.array(test_a_10)\n",
334 | "test_a_10 = test_a_10.reshape(len(test_a_10),)\n",
335 | "test_p_10 = np.array(test_p_10)\n",
336 | "test_p_10 = test_p_10.reshape(len(test_p_10),)\n",
337 | "test_a = test_a.reshape(len(test_a),)\n",
338 | "test_p = test_p.reshape(len(test_p),)\n",
339 | "\n",
340 | "\n",
341 | "\n",
342 | "train_a_10 = list()\n",
343 | "train_p_10 = list()\n",
344 | "for i in range(0, train_a.shape[0], 7):\n",
345 | " train_a_10.append(np.sum(train_a[i:i+7]))\n",
346 | " train_p_10.append(np.sum(train_p[i:i+7]))\n",
347 | "train_a_10 = np.array(train_a_10)\n",
348 | "train_a_10 = train_a_10.reshape(len(train_a_10),)\n",
349 | "train_p_10 = np.array(train_p_10)\n",
350 | "train_p_10 = train_p_10.reshape(len(train_p_10),)\n",
351 | "train_a = train_a.reshape(len(train_a),)\n",
352 | "train_p = train_p.reshape(len(train_p),)"
353 | ],
354 | "execution_count": 0,
355 | "outputs": []
356 | },
357 | {
358 | "cell_type": "code",
359 | "metadata": {
360 | "id": "cUwssMnaaQ_s",
361 | "colab_type": "code",
362 | "outputId": "127c55eb-1de8-40e3-a6a4-6612bb09fadf",
363 | "colab": {
364 | "base_uri": "https://localhost:8080/",
365 | "height": 53
366 | }
367 | },
368 | "source": [
369 | "from sklearn.metrics import mean_squared_error\n",
370 | "from sklearn.metrics import r2_score\n",
371 | "mse = mean_squared_error(test_a_10,test_p_10)\n",
372 | "print(\"Test_RMSE & R2\",np.sqrt(mse),\" \",r2_score(test_a_10,test_p_10))\n",
373 | "mse = mean_squared_error(train_a_10,train_p_10)\n",
374 | "print(\"Train_RMSE & R2\",np.sqrt(mse),\" \",r2_score(train_a_10,train_p_10))"
375 | ],
376 | "execution_count": 0,
377 | "outputs": [
378 | {
379 | "output_type": "stream",
380 | "text": [
381 | "Test_RMSE & R2 326.1861621016639 -0.6113293201258687\n",
382 | "Train_RMSE & R2 122.71954881000727 0.8449971812063375\n"
383 | ],
384 | "name": "stdout"
385 | }
386 | ]
387 | },
388 | {
389 | "cell_type": "code",
390 | "metadata": {
391 | "id": "8FTVCsOiaUi5",
392 | "colab_type": "code",
393 | "outputId": "80224f73-6789-4ae4-d082-e4f6dffa5208",
394 | "colab": {
395 | "base_uri": "https://localhost:8080/",
396 | "height": 887
397 | }
398 | },
399 | "source": [
400 | "import matplotlib.pyplot as plt\n",
401 | "%matplotlib inline\n",
402 | "plt.figure(figsize = (15,15))\n",
403 | "plt.plot(test_a_10,c ='g')\n",
404 | "plt.plot(test_p_10,c ='r')\n",
405 | "plt.show()"
406 | ],
407 | "execution_count": 0,
408 | "outputs": [
409 | {
410 | "output_type": "display_data",
411 | "data": {
412 | "image/png": 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AADhWSrhLJRmFAMAtwh0AAIBjvd0yE3TLBBAdwh0AAIBjpQwxb0oQ7gC4RbgDAABwjIYq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/o5Xj3+Klb+feXQe2ewUtVWBVsC0P/ID4GdO4VFY4yJQaDIVbmj52AzdwzfoRWH7du9f+3SpcQh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413 | "text/plain": [
414 | ""
415 | ]
416 | },
417 | "metadata": {
418 | "tags": []
419 | }
420 | }
421 | ]
422 | },
423 | {
424 | "cell_type": "code",
425 | "metadata": {
426 | "id": "j02T9kTYaY3m",
427 | "colab_type": "code",
428 | "colab": {}
429 | },
430 | "source": [
431 | "result_test = pd.DataFrame({'Y_Pred':test_p_10,'Y_True':test_a_10},columns = ['Y_Pred','Y_True'])\n",
432 | "result_train = pd.DataFrame({'Y_Pred':train_p_10,'Y_True':train_a_10},columns = ['Y_Pred','Y_True'])\n",
433 | "result_test.to_csv('weekly_result_test_A.csv')\n",
434 | "result_train.to_csv('weekly_result_train_A.csv')"
435 | ],
436 | "execution_count": 0,
437 | "outputs": []
438 | },
439 | {
440 | "cell_type": "code",
441 | "metadata": {
442 | "id": "eHGeissHlQSQ",
443 | "colab_type": "code",
444 | "colab": {}
445 | },
446 | "source": [
447 | ""
448 | ],
449 | "execution_count": 0,
450 | "outputs": []
451 | }
452 | ]
453 | }
--------------------------------------------------------------------------------