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zReQfQt2cHBXRN)|`SKpfk&$VOi8iYF!#vST&7;SB8IRQzS_if140^q}%f4FajV*ng z=fwD39T+9}NvWr0+Yn&OFQ@k;qNU===*y#FrpRmsV}4{&GtdE`+KO#^AO9)(hL-12 zV(G0j9}%y!ocO_Qk2b8Y)W_Wc_+6I|j(8Ix-bCnU?Cbwk?k&*yhu1Uw1>>3~qBj1K zf3ptjzbOA-R{6Fk_%%)gI$NCIcRpNF4j5bTV(f1>Lq1zdftX1W;aMh_VJY;@&6QP6 zA#3Uc7+B?+cAJqCR6wLZ>U2{af48}*5HFt)rS?xF@%Pz#0hUGrR3DG}F%-*q>8$th z;>j)iftJXmtw2WKtn^FG-yO0*x literal 0 HcmV?d00001 From b9543159862f604ebdc6e8f05c95c843ce62c7c4 Mon Sep 17 00:00:00 2001 From: Seyed Parsa Neshaei Date: Fri, 22 Jul 2022 22:38:18 +0200 Subject: [PATCH 2/2] Added global warming notebook Added references to CNN notebook --- notebooks/cnn/cnn.ipynb | 15 + notebooks/global-warming/global-warming.ipynb | 558 ++++++++++++++++++ 2 files changed, 573 insertions(+) create mode 100644 notebooks/global-warming/global-warming.ipynb diff --git a/notebooks/cnn/cnn.ipynb b/notebooks/cnn/cnn.ipynb index a705fa2..8f1447c 100644 --- a/notebooks/cnn/cnn.ipynb +++ b/notebooks/cnn/cnn.ipynb @@ -420,6 +420,21 @@ "path = \".....\" # fill this with your own path\n", "model.save(path) # save the model" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further reading and references\n", + "\n", + "- https://hackr.io/blog/keras-vs-tensorflow\n", + "- https://www.geeksforgeeks.org/cnn-introduction-to-padding/\n", + "- https://www.cs.toronto.edu/~kriz/cifar.html\n", + "- https://www.analyticsvidhya.com/blog/2021/03/introduction-to-batch-normalization/\n", + "- https://cnvrg.io/cnn-tensorflow/\n", + "- https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1\n", + "- https://www.v7labs.com/blog/computer-vision-applications#h6" + ] } ], "metadata": { diff --git a/notebooks/global-warming/global-warming.ipynb b/notebooks/global-warming/global-warming.ipynb new file mode 100644 index 0000000..2cf90ab --- /dev/null +++ b/notebooks/global-warming/global-warming.ipynb @@ -0,0 +1,558 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AI Applications in Global Warming\n", + "\n", + "Compiled by Seyed Parsa Neshaei" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "- Foreword\n", + "\n", + "- Introduction: What is global warming?\n", + "\n", + "- How can AI be used regarding global warming?\n", + "\n", + "- AI Application in-depth: Predicting CO2 Emission\n", + "\n", + "- Conclusion\n", + "\n", + "- Further reading and references" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Foreword\n", + "\n", + "Global warming is a serious issue in the world, leading to noticable climate change. Many ice caps are being melted, sea levels are being risen and rainfalls are happening less, due to the rise of the temperature.\n", + "\n", + "We, as a member of the society we're living in, have the responsibility to do whatever we can to reduce the risks by greenhouse gas emissions. In this notebook, we will get familiar with the problem of global warming, find out how can AI be used to tackle this issue and then we will see two applications of implementing AI on real-world datasets regarding global warming.\n", + "\n", + "So, let's dive in!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction: What is global warming?\n", + "\n", + "Global warming is an effect of the so-called greenhouse gases on the earth. These gases capture the sunlight, and as a result, an increase to the amount of these gases (including carbon dioxide and metane) in the atmosphere leads to a rise in the temperature of the surface of the earth.\n", + "\n", + "Consequently, water level with rise due to ice being caps melted and also rainfalls happen less. This climate change can have devastating effects on the natural environment, because many species are sensitive to even small changes in the normal temperature of the atmosphere.\n", + "\n", + "The increase in temperature also has several other consequences. For example, wildfires become more common, there would be more intense storms, droughts and acidic oceans, and all of this may cause human migration as a result. WHO has called this the greatest threat to global health in the 21st century." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How can AI be used regarding global warming?\n", + "\n", + "AI assists researchers in various fields, including the climate change and global warming. AI, data science, machine learning and deep learning are all useful in many fields, including the field of climate change, although the effect they have on climate change has not been explored as much as other fields were explored.\n", + "\n", + "Some key places an ML model can assist researchers in this regard are:\n", + "\n", + "- Modeling carbon sequestration\n", + " - This is a method to predict how much C (carbon) will be removed from the air and stored. The climate models can’t exacyly predict the climate in a thousand years, due to it being very sensitive to small changes in the average temperature. ML algorithms are being used to model the impact of carbon sequestration on climate change.\n", + "\n", + "\n", + "- Predict carbon dioxide emissions\n", + " - Here, ML algorithms are used to estimate how much CO2 can be emitted while still the climate remains within a given goal (i.e., staying under two degrees Celsius)\n", + " \n", + " \n", + "- Predicting greenhouse gas concentrations\n", + " - Predict future concentrations of greenhouse gases\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AI Application in-depth 1: Predicting CO2 Emission\n", + "\n", + "As an example, we want to predict CO2 emissions based on the previous data using an ML model. We will use a simple regression model to find trends in the data and predict CO2 emissions.\n", + "\n", + "First, let's load the data (co2.csv) which was obtained and changed a bit to be clarified from [here](https://datahub.io/core/co2-ppm#readme):" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# import necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# load the data in co2.csv using pandas\n", + "df = pd.read_csv('co2.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can inspect the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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01958-03-011958.208315.71
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" + ], + "text/plain": [ + " Date Decimal Date Average\n", + "0 1958-03-01 1958.208 315.71\n", + "1 1958-04-01 1958.292 317.45\n", + "2 1958-05-01 1958.375 317.50\n", + "3 1958-06-01 1958.458 -99.99\n", + "4 1958-07-01 1958.542 315.86" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This data shows the average CO2 emission in each date. Decimal Date is a column the creators of the dataset have added in order to make it easier for us to train a model.\n", + "\n", + "Let's drop the Date column since we don't need it anymore:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Decimal Date Average\n", + "0 1958.208 315.71\n", + "1 1958.292 317.45\n", + "2 1958.375 317.50\n", + "3 1958.458 -99.99\n", + "4 1958.542 315.86" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop the Date column from df\n", + "df.drop(\"Date\", axis=1, inplace=True)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the shape of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(727, 2)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can plot the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.scatter(x='Decimal Date', y='Average', title='CO2 Emissions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see there are some negative numbers in the dataset. They seem to be errors, so we remove them:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# remove any value less than 0 in the Average column of df\n", + "df = df[df['Average'] >= 0]\n", + "df.plot.scatter(x='Decimal Date', y='Average', title='CO2 Emissions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, as you've previously seen while implementing scikit models, we start to prepare the data and make the model: (we do the reshape because LinearRegression expects a 2D input)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "x = df['Decimal Date'].values.reshape(-1, 1)\n", + "y = df['Average'].values.reshape(-1, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We choose 20 percent for testing:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "Xtrain, Xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we finally fit the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "model = LinearRegression()\n", + "model.fit(Xtrain, ytrain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we evaluate the model on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Predicted Read\n", + "0 400.230985 410.24\n", + "1 390.480879 393.12\n", + "2 383.590285 381.99\n", + "3 323.529636 324.42\n", + "4 399.319935 403.37\n", + ".. ... ...\n", + "139 314.430057 321.39\n", + "140 345.889357 345.63\n", + "141 345.759876 346.03\n", + "142 367.729594 365.59\n", + "143 376.309686 373.79\n", + "\n", + "[144 rows x 2 columns]\n" + ] + } + ], + "source": [ + "preds = model.predict(Xtest)\n", + "preds_df = pd.DataFrame({'Predicted': preds.squeeze(), 'Read': ytest.squeeze()})\n", + "print(preds_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result are similar to the real values, which means our model has been mostly successful.\n", + "\n", + "The model can certainly be improved by using more advanced methods such as ANNs, but the goal here basically was to see how much a simple linear regression model may be useful in such a task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we learned about the problems regarding the climate change and how it can affect our lives, then studied some AI applications related to the global warming. We implemented a simple piece of code to show how simply we can perform an analysis of the available data on the internet to get insight about the environmental problems and predict metrics for the future, assuming the trends don't change in a noticable, significant manner." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further reading and references\n", + "\n", + "- https://datahub.io/awesome/climate-change\n", + "\n", + "- https://en.wikipedia.org/wiki/Climate_change\n", + "\n", + "- https://vitalflux.com/machine-learning-use-cases-climate-change/\n", + "\n", + "- https://data.world/datasets/global-warming\n", + "\n", + "- https://www.analyticssteps.com/blogs/8-ways-which-ai-tackles-climate-change\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('env_tf')", + "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": "3.9.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8e74460d205db1d36a715e76f12628a0e4a7d4b0adb185ac4570d73967b15f9b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}