Generative weather for improved crop model simulations
- URL: http://arxiv.org/abs/2404.00528v1
- Date: Sun, 31 Mar 2024 02:03:28 GMT
- Title: Generative weather for improved crop model simulations
- Authors: Yuji Saikai,
- Abstract summary: We propose a new method to construct generative models for long-term weather forecasts.
Results show significant improvement from the conventional method.
For individual crop modellers to start applying the method to their problems, technical details are carefully explained.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Accurate and precise crop yield prediction is invaluable for decision making at both farm levels and regional levels. To make yield prediction, crop models are widely used for their capability to simulate hypothetical scenarios. While accuracy and precision of yield prediction critically depend on weather inputs to simulations, surprisingly little attention has been paid to preparing weather inputs. We propose a new method to construct generative models for long-term weather forecasts and ultimately improve crop yield prediction. We demonstrate use of the method in two representative scenarios -- single-year production of wheat, barley and canola and three-year production using rotations of these crops. Results show significant improvement from the conventional method, measured in terms of mean and standard deviation of prediction errors. Our method outperformed the conventional method in every one of 18 metrics for the first scenario and in 29 out of 36 metrics for the second scenario. For individual crop modellers to start applying the method to their problems, technical details are carefully explained, and all the code, trained PyTorch models, APSIM simulation files and result data are made available.
Related papers
- PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling [85.56969895866243]
We propose an unsupervised postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth.
A zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy are introduced to adapt the unconditional correlations to any blurriness modes.
arXiv Detail & Related papers (2024-10-08T08:38:23Z) - Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning.
By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
arXiv Detail & Related papers (2024-09-10T07:34:19Z) - Predictive Churn with the Set of Good Models [64.05949860750235]
We study the effect of conflicting predictions over the set of near-optimal machine learning models.
We present theoretical results on the expected churn between models within the Rashomon set.
We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
arXiv Detail & Related papers (2024-02-12T16:15:25Z) - ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast.
We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples.
Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
arXiv Detail & Related papers (2024-02-02T10:34:13Z) - 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) - Integrating processed-based models and machine learning for crop yield
prediction [1.3107669223114085]
In this work we investigate potato yield prediction using a hybrid meta-modeling approach.
A crop growth model is employed to generate synthetic data for (pre)training a convolutional neural net.
When applied in silico, our meta-modeling approach yields better predictions than a baseline comprising a purely data-driven approach.
arXiv Detail & Related papers (2023-07-25T12:51:25Z) - A GNN-RNN Approach for Harnessing Geospatial and Temporal Information:
Application to Crop Yield Prediction [18.981160729510417]
We introduce a novel graph-based recurrent neural network for crop yield prediction, to incorporate both geographical and temporal knowledge.
Our method is trained, validated, and tested on over 2000 counties from 41 states in the US mainland, covering years from 1981 to 2019.
arXiv Detail & Related papers (2021-11-17T04:43:25Z) - Comparison of Machine Learning Methods for Predicting Winter Wheat Yield
in Germany [0.0]
This study analyzed the performance of different machine learning methods for winter wheat yield prediction.
To address the seasonality, weekly features were used that explicitly take soil moisture conditions and meteorological events into account.
arXiv Detail & Related papers (2021-05-04T04:40:53Z) - Improving Maximum Likelihood Training for Text Generation with Density
Ratio Estimation [51.091890311312085]
We propose a new training scheme for auto-regressive sequence generative models, which is effective and stable when operating at large sample space encountered in text generation.
Our method stably outperforms Maximum Likelihood Estimation and other state-of-the-art sequence generative models in terms of both quality and diversity.
arXiv Detail & Related papers (2020-07-12T15:31:24Z) - Crop Yield Prediction Integrating Genotype and Weather Variables Using
Deep Learning [8.786816847837976]
We use historical performance records from Uniform Soybean Tests (UST) in North America spanning 13 years of data to build a Long Short Term Memory - Recurrent Neural Network based model to dissect and predict genotype response in multiple environments.
We deploy this deep learning framework as a 'hypotheses generation tool' to unravel GxExM relationships.
We envision broad applicability of this approach (via conducting sensitivity analysis and "what-if" scenarios) for soybean and other crop species under different climatic conditions.
arXiv Detail & Related papers (2020-06-24T16:20:12Z) - Forecasting Corn Yield with Machine Learning Ensembles [2.9005223064604078]
This paper provides a machine learning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa)
Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions.
Results show that ensemble models based on weighted average of the base learners outperform individual models.
arXiv Detail & Related papers (2020-01-18T03:55:20Z)
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.