From 041958e68d74c56c00ee990326d00ac17a382337 Mon Sep 17 00:00:00 2001 From: Mark Bakker Date: Wed, 20 Dec 2023 14:42:15 +0100 Subject: [PATCH] added howto pumpintests --- docs/01howto/data/oudekorendijk_h30.dat | 34 + docs/01howto/data/oudekorendijk_h90.dat | 35 + docs/01howto/howto_pumpingtest.ipynb | 964 ++++++++++++++++++ docs/01howto/index.rst | 8 +- .../1_test_of_oude_korendijk.ipynb | 2 +- 5 files changed, 1038 insertions(+), 5 deletions(-) create mode 100755 docs/01howto/data/oudekorendijk_h30.dat create mode 100755 docs/01howto/data/oudekorendijk_h90.dat create mode 100755 docs/01howto/howto_pumpingtest.ipynb diff --git a/docs/01howto/data/oudekorendijk_h30.dat b/docs/01howto/data/oudekorendijk_h30.dat new file mode 100755 index 0000000..9641725 --- /dev/null +++ b/docs/01howto/data/oudekorendijk_h30.dat @@ -0,0 +1,34 @@ +0.1 0.04 +0.25 0.08 +0.50 0.13 +0.70 0.18 +1.0 0.23 +1.40 0.28 +1.90 0.33 +2.33 0.36 +2.80 0.39 +3.36 0.42 +4.00 0.45 +5.35 0.50 +6.80 0.54 +8.3 0.57 +8.7 0.58 +10.0 0.60 +13.1 0.64 + 18 0.680 + 27 0.742 + 33 0.753 + 41 0.779 + 48 0.793 + 59 0.819 + 80 0.855 + 95 0.873 +139 0.915 +181 0.935 +245 0.966 +300 0.990 +360 1.007 +480 1.050 +600 1.053 +728 1.072 +830 1.088 diff --git a/docs/01howto/data/oudekorendijk_h90.dat b/docs/01howto/data/oudekorendijk_h90.dat new file mode 100755 index 0000000..bfb6882 --- /dev/null +++ b/docs/01howto/data/oudekorendijk_h90.dat @@ -0,0 +1,35 @@ +1.5 0.015 +2.0 0.021 +2.16 0.023 +2.66 0.044 +3 0.054 +3.5 0.075 +4 0.090 +4.33 0.104 +5.5 0.133 +6 0.153 +7.5 0.178 +9 0.206 +13 0.250 +15 0.275 +18 0.305 +25 0.348 +30 0.364 +40 0.404 +53 0.429 +60 0.444 +75 0.467 +90 0.494 +105 0.507 +120 0.528 +150 0.550 +180 0.569 +248 0.593 +301 0.614 +363 0.636 +422 0.657 +542 0.679 +602 0.688 +680 0.701 +785 0.718 +845 0.716 \ No newline at end of file diff --git a/docs/01howto/howto_pumpingtest.ipynb b/docs/01howto/howto_pumpingtest.ipynb new file mode 100755 index 0000000..15f6def --- /dev/null +++ b/docs/01howto/howto_pumpingtest.ipynb @@ -0,0 +1,964 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### HowTo: Pumping test analysis with `ttim`\n", + " \n", + "This HowTo describes how to use `ttim` for pumping test analysis (also called aquifer test analysis).\n", + "A simple pumping test is analyzed in this Notebook. A whole series of notebooks with examples of pumping test analyses are listed under the 'pumping test benchmark' section of the ReadTheDocs. \n", + "\n", + "We start by importing the required packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ttim as ttm\n", + "#\n", + "plt.rcParams['figure.figsize'] = (4, 3) # set default figure size\n", + "plt.rcParams['font.size'] = 8 # set default font size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We analyze pumping test data from a pumping test carried out near the *Oude Korendijk* in The Netherlands, as described in the famous book on pumping tests by Kruseman and de Ridder (downloadable [here](https://gw-project.org/download/analysis-and-evaluation-of-pumping-test-data/)). The pumping test was carried out in a 7 m thick, confined aquifer with the top at $z_t=-18$ m and the bottom at $z_b=-25$ m. The objective of the pumping test was to determine the hydraulic conductivity and specific storage coefficient of the aquifer. The discharge of the well during the pumping test was 788 m$^3$/day. The observed drawdown (in meters) and the observation time (in minutes) after pumping started were measured in two observations wells, one located 30 m from the pumping well and the other located 90 m from the pumping well. Kruseman and de Ridder (1990) also report drawdown in an additional well, but that one is not considered here. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The time (in minutes) and drawdown (in meters) of the two wells are stored in the files `data/oudekorendijk_h30.dat` and `data/oudekorendijk_h90.dat`, respectively. The time and head are read from the data files. The time is converted to days and the drawdown is converted to meters, after which the data is plotted." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first measurement time: 6.94e-05, last measurement time: 5.76e-01 d\n", + "first measurement time: 1.04e-03, last measurement time: 5.87e-01 d\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = np.loadtxt('data/oudekorendijk_h30.dat')\n", + "to1 = data[:, 0] / 60 / 24\n", + "ho1 = -data[:, 1]\n", + "ro1 = 30\n", + "print(f'first measurement time: {np.min(to1):.2e}, last measurement time: {np.max(to1):.2e} d')\n", + "\n", + "drawdown = np.loadtxt('data/oudekorendijk_h90.dat')\n", + "to2 = drawdown[:, 0] / 60 / 24\n", + "ho2 = -drawdown[:, 1]\n", + "ro2 = 90\n", + "print(f'first measurement time: {np.min(to2):.2e}, last measurement time: {np.max(to2):.2e} d')\n", + "\n", + "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", + "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A pumping test analysis with `ttim` consists of the following steps:\n", + "1. Create a `ttim` model using your best guess of the parameters.\n", + "2. Create a `Calibrate object`\n", + "3. Specify which parameters you want to estimate.\n", + "4. Specify which time serie(s) you want to use to estimate the parameters.\n", + "5. Estimate the parameters\n", + "\n", + "The parameters are fitted using the least squares method. An iterative approach is used to find the optimal parameters. The iterative approach is pretty fancy, but no iterative method is foolproof. The better your guess of the parameters, the higher the likelihood that the iterative method will find the optimal parameters (no pun intended). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We creat a model for a single confined aquifer. The initial guess is that the hydraulic conductivity is $k=20$ m/d and the specific storage coefficient is $S_s=10^{-4}$ m$^{-1}$. `tmin` and `tmax` are set to cover all observation times. One well is added with a discharge of 788 m$^3$/d. No recovery was measured, so the well does not have to be shut off. Wellbore storage is not simulated. The model is solved. As a check, the head is plotted vs. the observations. This confirms that the model runs. As can be seen, the initial guess is not bad, but obviously not optimal. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = ttm.ModelMaq(kaq=60, \n", + " z=(-18, -25), \n", + " Saq=1e-4, \n", + " tmin=1e-5, \n", + " tmax=1)\n", + "w = ttm.Well(model=ml, \n", + " xw=0, \n", + " yw=0, \n", + " rw=0.1, \n", + " tsandQ=[(0, 788)], \n", + " layers=0)\n", + "ml.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml.head(ro1, 0, to1)\n", + "hm2 = ml.head(ro2, 0, to2)\n", + "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", + "plt.plot(to1, hm1[0], 'C0')\n", + "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", + "plt.plot(to2, hm2[0], 'C1')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Steps 2-5 are conducted in the next code block. At first, only the observations in the first well (at 30 m from the pumping well) are used for calibration.\n", + "\n", + "2. The `Calibration` object is created for the model `ml` (which was created above). The `Calibration` object is stored in the variable `cal`. \n", + "3. It is specified that the `Calibration` object must fit for the hydraulic conductivity of layer 0 by using the `set_parameter` function. The name of the parameters is `kaq` (the same name as used to create the `ttim` model) and the `0` is added to indicate that it is the hydraulic conductivity of layer 0. Furthermore, the initial value is specified. Again, use your best guess. Next, it is specified to fit for the specific discharge of layer 0 in the same fashio. The name of the parameter is `Saq` (again, the same name as used to create the `ttim` model) and the `0` is added to indicate layer 0. \n", + "4. It is specified to use the times and heads of the observation well at $r=30$ m using the `series` function. The `series` function takes a name (use whatever name you like to identify this observation well), the `x` and `y` location of the well, the layer number, an array of the observation times and an array of the observed heads.\n", + "5. Find the optimal parameters using the `fit` function. A dot is printed to the screen every time the model is solved with a new set of parameters during the iterative optimization approach. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".........................\n", + "Fit succeeded.\n" + ] + } + ], + "source": [ + "cal = ttm.Calibrate(model=ml) # create Calibration object\n", + "cal.set_parameter(name='kaq0', initial=20) # \n", + "cal.set_parameter(name='Saq0', initial=1e-4)\n", + "cal.series(name='obs1', # user specified name\n", + " x=ro1, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to1, # observation times\n", + " h=ho1 # observed heads\n", + " )\n", + "cal.fit(report=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A good approach to print the fitted parameters to the screen is to display the `cal.parameters` `DataFrame`. Note that the estimated standard error (the column `std`) is **not** a very good estimate of the standard error, as the remaining errors rarely statisfy the required statistical conditions for such an estimate of the standard error." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq068.6395791.4381672.095244-infinf20.0000[68.63957893530642]
Saq00.0000160.0000029.844424-infinf0.0001[1.6071364767106862e-05]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 68.639579 1.438167 2.095244 -inf inf 20.0000 \n", + "Saq0 0.000016 0.000002 9.844424 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [68.63957893530642] \n", + "Saq0 [1.6071364767106862e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(cal.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The optimal parameters are also automatically stored in the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k of model: [68.63957894]\n", + "Ss of model: [1.60713648e-05]\n" + ] + } + ], + "source": [ + "print('k of model: ', ml.aq.kaq)\n", + "print('Ss of model: ', ml.aq.Saq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hence, a graph of the model fit can be created in the same fashion as before. Note that the fit for the observations at $r=30$ m is indeed a lot better than with our initial guess." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml.head(ro1, 0, to1)\n", + "plt.plot(to1, ho1, 'k.', label='r=30 m')\n", + "plt.plot(to1, hm1[0], 'C0', label='fit')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model with the optimal parameters deviates significantly from the obervations at larger times. Maybe the confining top layer can not be simulated as impereable. Let's modify the model and replace the confined aquifer by a semi-confined aquifer. The new model is called `ml2`. The thickness of the semi-confining layer is set to 1 (i.e., `z` goes from -17 to -18) and the initial guess for the resistance of the semi-confining layer is 1000 d. Storage in the semi-confining layer is negelected. A new calibration object is created, which is used now to fit three parameters. The additional parameter is called `'c0'`, which stands for the resistance of leaky layer 0, which is on top of aquifer layer 0. Note that the optimal hydraulic conductivity is now more than 10% lower than for the confined aquifer. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n", + "..............................................\n", + "Fit succeeded.\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq059.4003511.2128272.041784-infinf20.0000[59.400350934475114]
Saq00.0000220.0000015.811408-infinf0.0001[2.229668504069974e-05]
c02084.749713376.12038318.041513-infinf1000.0000[2084.7497129326725]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 59.400351 1.212827 2.041784 -inf inf 20.0000 \n", + "Saq0 0.000022 0.000001 5.811408 -inf inf 0.0001 \n", + "c0 2084.749713 376.120383 18.041513 -inf inf 1000.0000 \n", + "\n", + " parray \n", + "kaq0 [59.400350934475114] \n", + "Saq0 [2.229668504069974e-05] \n", + "c0 [2084.7497129326725] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ml2 = ttm.ModelMaq(kaq=60, \n", + " z=(-17, -18, -25), \n", + " Saq=1e-4, \n", + " c=[1000],\n", + " Sll=0,\n", + " topboundary='semi',\n", + " tmin=1e-5, \n", + " tmax=1)\n", + "w = ttm.Well(model=ml2, \n", + " xw=0, \n", + " yw=0, \n", + " rw=0.1, \n", + " tsandQ=[(0, 788)], \n", + " layers=0)\n", + "ml2.solve()\n", + "#\n", + "cal2 = ttm.Calibrate(model=ml2) # create Calibration object\n", + "cal2.set_parameter(name='kaq0', initial=20) # \n", + "cal2.set_parameter(name='Saq0', initial=1e-4)\n", + "cal2.set_parameter(name='c0', initial=1000)\n", + "cal2.series(name='obs1', # user specified name\n", + " x=ro1, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to1, # observation times\n", + " h=ho1 # observed heads\n", + " )\n", + "cal2.fit(report=False)\n", + "display(cal2.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fit of the two models can be compared visually:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml.head(ro1, 0, to1)\n", + "hm2 = ml2.head(ro1, 0, to1)\n", + "plt.plot(to1, ho1, 'k.', label='r=30 m')\n", + "plt.plot(to1, hm1[0], label='fit 2 params')\n", + "plt.plot(to1, hm2[0], label='fit 3 params')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or the fit of the two models can be compared by computing the root mean squared error for the two models. Note that addition of the semi-confining layer almost halved the root mean squared error. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse 2 params: 0.032 m\n", + "rmse 3 params: 0.017 m\n" + ] + } + ], + "source": [ + "print(f'rmse 2 params: {cal.rmse():.3f} m')\n", + "print(f'rmse 3 params: {cal2.rmse():.3f} m')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The root mean squared error can be computed with the `rmse` function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the two models are fitted to the observations in both observation wells. This is done by adding a second series to both `cal` and `cal2` and re-fitting the model. The root mean squared error are printed to the screen and graphs of the model fit are produced." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "............................\n", + "Fit succeeded.\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq066.0898841.6549782.504132-infinf20.0000[66.08988416130906]
Saq00.0000250.0000029.451760-infinf0.0001[2.5407665925649665e-05]
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" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 66.089884 1.654978 2.504132 -inf inf 20.0000 \n", + "Saq0 0.000025 0.000002 9.451760 -inf inf 0.0001 \n", + "\n", + " parray \n", + "kaq0 [66.08988416130906] \n", + "Saq0 [2.5407665925649665e-05] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cal.series(name='obs2', # user specified name\n", + " x=ro2, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to2, # observation times\n", + " h=ho2 # observed heads\n", + " )\n", + "cal.fit(report=False)\n", + "display(cal.parameters)\n", + "print" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n" + ] + }, + { + "data": { + "text/html": [ + "
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optimalstdperc_stdpminpmaxinitialparray
kaq053.7301521.0054281.871254-infinf20.0000[53.730152170965056]
Saq00.0000320.0000014.203155-infinf0.0001[3.157755882214844e-05]
c01016.334627108.35192110.661048-infinf1000.0000[1016.3346266308762]
\n", + "
" + ], + "text/plain": [ + " optimal std perc_std pmin pmax initial \\\n", + "kaq0 53.730152 1.005428 1.871254 -inf inf 20.0000 \n", + "Saq0 0.000032 0.000001 4.203155 -inf inf 0.0001 \n", + "c0 1016.334627 108.351921 10.661048 -inf inf 1000.0000 \n", + "\n", + " parray \n", + "kaq0 [53.730152170965056] \n", + "Saq0 [3.157755882214844e-05] \n", + "c0 [1016.3346266308762] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cal2.series(name='obs2', # user specified name\n", + " x=ro2, # x=location of observation well\n", + " y=0, # y-location of obsevation well\n", + " layer=0, # layer number\n", + " t=to2, # observation times\n", + " h=ho2 # observed heads\n", + " )\n", + "cal2.fit(report=False)\n", + "display(cal2.parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rmse 2 params: 0.050 m\n", + "rmse 3 params: 0.025 m\n" + ] + } + ], + "source": [ + "print(f'rmse 2 params: {cal.rmse():.3f} m')\n", + "print(f'rmse 3 params: {cal2.rmse():.3f} m')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 3))\n", + "plt.subplot(121)\n", + "hm1 = ml.head(ro1, 0, to1)\n", + "hm2 = ml.head(ro2, 0, to2)\n", + "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", + "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", + "plt.plot(to1, hm1[0], 'C0', label='fit 2 params')\n", + "plt.plot(to2, hm2[0], 'C1', label='fit 2 params')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()\n", + "#\n", + "plt.subplot(122)\n", + "hm1 = ml2.head(ro1, 0, to1)\n", + "hm2 = ml2.head(ro2, 0, to2)\n", + "plt.plot(to1, ho1, 'C0.', label='r=30 m')\n", + "plt.plot(to2, ho2, 'C1.', label='r=90 m')\n", + "plt.plot(to1, hm1[0], 'C0', label='fit 3 params')\n", + "plt.plot(to2, hm2[0], 'C1', label='fit 3 params')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insight of the model fit can also be gained by plotting the results on semi-log graph." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 3))\n", + "plt.subplot(121)\n", + "hm1 = ml.head(ro1, 0, to1)\n", + "hm2 = ml.head(ro2, 0, to2)\n", + "plt.semilogx(to1, ho1, 'C0.', label='r=30 m')\n", + "plt.semilogx(to2, ho2, 'C1.', label='r=90 m')\n", + "plt.semilogx(to1, hm1[0], 'C0', label='fit 2 params')\n", + "plt.semilogx(to2, hm2[0], 'C1', label='fit 2 params')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()\n", + "#\n", + "plt.subplot(122)\n", + "hm1 = ml2.head(ro1, 0, to1)\n", + "hm2 = ml2.head(ro2, 0, to2)\n", + "plt.semilogx(to1, ho1, 'C0.', label='r=30 m')\n", + "plt.semilogx(to2, ho2, 'C1.', label='r=90 m')\n", + "plt.semilogx(to1, hm1[0], 'C0', label='fit 3 params')\n", + "plt.semilogx(to2, hm2[0], 'C1', label='fit 3 params')\n", + "plt.xlabel('time (d)')\n", + "plt.ylabel('head (m)')\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is now up to the modeler to decide which model is better and whether a semi-confined aquifer is more realistic than a confined aquifer. The model with 3 parameters gives a better fit in terms of the root mean squared error, but the behavior for large time deviates from the observations: the observed heads seem to go down with time in the semi-log graphs, but the modeled heads seem to level off for large time." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/01howto/index.rst b/docs/01howto/index.rst index ab37023..8911e70 100644 --- a/docs/01howto/index.rst +++ b/docs/01howto/index.rst @@ -1,7 +1,7 @@ -User Guide -========== +How To Guides +============= -This section contains the basic concepts of building groundwater models with TTim. +This section contains how to guides on building groundwater models with TTim. .. toctree:: @@ -9,4 +9,4 @@ This section contains the basic concepts of building groundwater models with TTi getting_started/index.rst elements/index.rst - \ No newline at end of file + howto_pumpingtest \ No newline at end of file diff --git a/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb b/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb index 0a016f7..c16e960 100755 --- a/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb +++ b/docs/04pumpingtests/1_test_of_oude_korendijk.ipynb @@ -1279,7 +1279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.4" }, "latex_envs": { "LaTeX_envs_menu_present": true,