Forecasting Drought Using Machine Learning in California

• URL: http://arxiv.org/abs/2502.08622v1
• Date: Wed, 12 Feb 2025 18:20:41 GMT
• Title: Forecasting Drought Using Machine Learning in California
• Authors: Nan K. Li, Angela Chang, David Sherman,
• Abstract summary: Drought is a frequent and costly natural disaster in California, with major negative impacts on agricultural production and water resource availability. This study investigated the performance of applying different machine learning approaches to predicting the U.S. Drought Monitor classification in California.
• Score: 0.16385815610837165
• License:
• Abstract: Drought is a frequent and costly natural disaster in California, with major negative impacts on agricultural production and water resource availability, particularly groundwater. This study investigated the performance of applying different machine learning approaches to predicting the U.S. Drought Monitor classification in California. Four approaches were used: a convolutional neural network (CNN), random forest, XGBoost, and long short term memory (LSTM) recurrent neural network, and compared to a baseline persistence model. We evaluated the models' performance in predicting severe drought (USDM drought category D2 or higher) using a macro F1 binary classification metric. The LSTM model emerged as the top performer, followed by XGBoost, CNN, and random forest. Further evaluation of our results at the county level suggested that the LSTM model would perform best in counties with more consistent drought patterns and where severe drought was more common, and the LSTM model would perform worse where drought scores increased rapidly. Utilizing 30 weeks of historical data, the LSTM model successfully forecasted drought scores for a 12-week period with a Mean Absolute Error (MAE) of 0.33, equivalent to less than half a drought category on a scale of 0 to 5. Additionally, the LSTM achieved a macro F1 score of 0.9, indicating high accuracy in binary classification for severe drought conditions. Evaluation of different window and future horizon sizes in weeks suggested that at least 24 weeks of data would result in the best performance, with best performance for shorter horizon sizes, particularly less than eight weeks.

Related papers

• LLMs & XAI for Water Sustainability: Seasonal Water Quality Prediction with LIME Explainable AI and a RAG-based Chatbot for Insights [0.0]
  This paper introduces a hybrid deep learning model to predict Nepal's seasonal water quality using a small dataset with multiple water quality parameters. CatBoost, XGBoost, Extra Trees, and LightGBM, along with a neural network combining CNN and RNN layers, are used to capture temporal and spatial patterns in the data. The model demonstrated notable accuracy improvements, aiding proactive water quality control.
  arXiv  Detail & Related papers  (2024-09-17T05:26:59Z)
• Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression [9.816891579613628]
  A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the Central Valley aquifer. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.
  arXiv  Detail & Related papers  (2023-10-23T04:21:26Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Rapid Flood Inundation Forecast Using Fourier Neural Operator [77.30160833875513]
  Flood inundation forecast provides critical information for emergency planning before and during flood events. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
  arXiv  Detail & Related papers  (2023-07-29T22:49:50Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model [9.7723814375467]
  A self-screening graph convolutional network and long short-term memory network (SGCN-LSTM) is proposed in this paper. The landslide samples with large errors outside the set threshold interval are eliminated by self-screening network. The nonlinear relationship between environmental factors can be extracted from both spatial nodes and time series.
  arXiv  Detail & Related papers  (2023-04-12T10:31:03Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM [0.0]
  In this paper, a multi-layer perceptron is applied to simulate groundwater level. The adaptive moment estimation algorithm is also used to this matter. Results indicate that deep learning algorithms can demonstrate a high accuracy prediction.
  arXiv  Detail & Related papers  (2021-07-29T10:11:45Z)
• High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks [0.03694429692322631]
  The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time.
  arXiv  Detail & Related papers  (2020-02-07T00:38:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.