ML Approach

ML Approach#

General steps to build and deploy ML models#

Generally, machine learning approaches leverage advanced algorithms to predict SWE values based on historical data and various meteorological parameters.

Data collection#

We need to collect historical SWE measurements, meteorological data, and other factors that influence snow accumulation and melt. Sources of data may include ground-based observations, remote sensing data, climate records, and numerical weather prediction models.

Preprocessing#

Once the data is collected, it undergoes preprocessing. This step involves data cleaning, handling missing values, and standardizing data formats. For instance, data from different sources may have variations in quality and format, which need to be harmonized for machine learning algorithms to work effectively.

Feature Engineering#

Feature engineering is a crucial step where domain knowledge plays a significant role. It involves selecting the most relevant variables (features) that impact SWE. Features might include temperature, precipitation, solar radiation, elevation, and more. Feature selection and extraction aim to reduce dimensionality while preserving essential information.

Model selection#

Common models used for SWE forecasting include:

  • Regression Models: Linear regression, polynomial regression, or more complex regression models can be used to predict SWE values based on features.

  • Time Series Models: Models like ARIMA (AutoRegressive Integrated Moving Average) are suitable for capturing temporal patterns in SWE data.

  • Decision Trees and Random Forests: These models can handle both numerical and categorical features and capture complex relationships in the data.

  • Support Vector Machines (SVM): SVMs can be used for regression tasks, aiming to find a hyperplane that best fits the data.

  • Neural Networks: Deep learning models, including feedforward neural networks, recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), can capture intricate patterns in the data.

Train the model#

With the selected model, the dataset is divided into a training set and a testing set. The training set is used to train the model, allowing it to learn the relationships between features and SWE values. The model’s parameters are adjusted to minimize the error between predicted and actual SWE values.

Model evaluation#

The testing set is used to evaluate the model’s performance. Common evaluation metrics include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2). The goal is to ensure the model’s accuracy and assess its ability to generalize to unseen data.

Hyperparameter tuning#

Hyperparameters, which are not learned during training, need to be optimized to improve the model’s performance. Techniques like grid search or random search are used to find the best combination of hyperparameters.

Validation and Cross-Validation#

To ensure the model’s robustness, cross-validation techniques such as k-fold cross-validation are applied. This involves dividing the data into subsets and repeatedly training and testing the model on different combinations.

Operational predictions and forecasting#

Once the model is trained and validated, it can make predictions about future SWE values. This is particularly useful for forecasting SWE in the coming weeks, months, or even years.

Continuous monitoring and maintanence#

Machine learning models for SWE forecasting are not static; they need to be regularly updated with new data to adapt to changing conditions and improve accuracy.

  • However, it seems easy to talk about it. Doing it is totally a different thing. There are a lot of explicit and hidden traps waiting for you, and it is very easy to run into rabbitholes. Before we even start, we will introduce some of our silver-bullet tools to prevent us from wasting time in this week.

Contents