Merge pull request #22 from JYOSHREDDY/main

Motivation chapter

book/chapters/motivation.ipynb

@@ -0,0 +1,65 @@
+{
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+  {
+   "cell_type": "markdown",
+   "id": "671e2711",
+   "metadata": {},
+   "source": [
+    "# Motivation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30fcdf0d",
+   "metadata": {},
+   "source": [
+    "Accurately predicting Snow Water Equivalent (SWE) is important for many areas like agriculture, water management, and infrastructure planning. As climate change continues to affect snowpack patterns, traditional methods of forecasting are becoming less reliable. These older methods often struggle to deal with the changes in snow conditions, leading to mistakes in predictions that can seriously affect how we manage water supplies, prepare for floods, and plan agricultural activities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e2a609",
+   "metadata": {},
+   "source": [
+    "Machine Learning (ML) offers a new way to tackle these challenges by using advanced techniques to handle large and complex datasets, improving the accuracy of SWE forecasts. But using ML isn’t easy. Researchers face many challenges, such as dealing with unpredictable data, creating complicated workflows, and keeping track of past experiments. For example, snowpack data can vary a lot due to differences in elevation, temperature changes, and different types of precipitation. This can cause models to be too specific to certain conditions, making them less useful in other situations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f31d4f6f",
+   "metadata": {},
+   "source": [
+    "Also, without standard ways to manage ML experiments, even small changes in data or model settings can lead to very different results. This makes it harder for scientists to build models that can be reused, slowing down progress in SWE research because they have to keep rechecking and refining their work instead of applying their findings to real-world problems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db598b59",
+   "metadata": {},
+   "source": [
+    "The need for this research is urgent because climate-related events, like unexpected snowmelt causing water shortages or floods, are happening more often and with greater severity. For instance, in recent years, places that rely on snowmelt for their water supply have struggled because traditional prediction methods couldn’t predict early or fast snowmelt, leading to droughts or floods. As these events become more common, the demand for accurate and timely SWE forecasts is growing, making it essential to find new ways to keep up with these environmental changes. By making ML-based forecasting methods more efficient, easier to use, and adaptable to different research needs, geoweaver aims to create better workflows that improve the accuracy of SWE forecasts. The ultimate goal is to help ensure that we can manage our natural resources more sustainably and make better decisions in areas that depend on snow."
+   ]
+  }
+ ],
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