A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology

• URL: http://arxiv.org/abs/2405.13646v1
• Date: Wed, 22 May 2024 13:50:42 GMT
• Title: A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology
• Authors: Mingyu Liu, Nana Bao, Xingting Yan, Chenyang Li, Kai Peng,
• Abstract summary: Transformer, as one kind of the cutting-edge deep learning methods, offers an effective approach to model intricate nonlinear processes. XAI methods play important roles in enhancing the understandings of how different factors impact water level.
• Score: 14.006797311342753
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding the combined influences of meteorological and hydrological factors on water level and flood events is essential, particularly in today's changing climate environments. Transformer, as one kind of the cutting-edge deep learning methods, offers an effective approach to model intricate nonlinear processes, enables the extraction of key features and water level predictions. EXplainable Artificial Intelligence (XAI) methods play important roles in enhancing the understandings of how different factors impact water level. In this study, we propose a Transformer variant by integrating sparse attention mechanism and introducing nonlinear output layer for the decoder module. The variant model is utilized for multi-step forecasting of water level, by considering meteorological and hydrological factors simultaneously. It is shown that the variant model outperforms traditional Transformer across different lead times with respect to various evaluation metrics. The sensitivity analyses based on XAI technology demonstrate the significant influence of meteorological factors on water level evolution, in which temperature is shown to be the most dominant meteorological factor. Therefore, incorporating both meteorological and hydrological factors is necessary for reliable hydrological prediction and flood prevention. In the meantime, XAI technology provides insights into certain predictions, which is beneficial for understanding the prediction results and evaluating the reasonability.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Approaches for enhancing extrapolability in process-based and data-driven models in hydrology [0.16735447464058464]
  This paper reviews and compares methods for assessing and enhancing the extrapolability of process-based and data-driven hydrological models. Key strategies include the use of leave-one-out cross-validation and similarity-based methods to evaluate model performance in ungauged regions. Deep learning, transfer learning, and domain adaptation techniques are also promising in their potential to improve model predictions in data-sparse and extreme conditions.
  arXiv  Detail & Related papers  (2024-08-13T17:59:24Z)
• VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting [58.12667617617306]
  We propose VegeDiff for the geospatial vegetation forecasting task. VegeDiff is the first to employ a diffusion model to probabilistically capture the uncertainties in vegetation change processes. By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods.
  arXiv  Detail & Related papers  (2024-07-17T14:15:52Z)
• Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates [2.822521762044523]
  This paper investigates the application of Transformer-based models for predicting drum water levels in a steam boiler plant. To this end, a prudent pipeline is proposed, including 1) data preprocess, 2) causal relation analysis, 3) delay inference, 4) variable augmentation, and 5) prediction.
  arXiv  Detail & Related papers  (2024-07-15T19:05:30Z)
• TRG-Net: An Interpretable and Controllable Rain Generator [61.2760968459789]
  This study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, but also finely adapt to complicated and diverse practical rainy images. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T03:27:39Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting [4.915744683251151]
  Hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. We propose atemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder. We present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes.
  arXiv  Detail & Related papers  (2023-12-10T18:23:40Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Heterogeneous Stream-reservoir Graph Networks with Data Assimilation [3.312798619476657]
  Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. We propose a heterogeneous recurrent graph model to represent interacting processes that underlie stream-reservoir networks. Because reservoir release data may be unavailable for certain reservoirs, we develop a data assimilation mechanism to correct for the prediction bias caused by reservoir releases.
  arXiv  Detail & Related papers  (2021-10-11T01:47:16Z)
• Assessment of Neural Networks for Stream-Water-Temperature Prediction [0.0]
  A mechanistic understanding of the drivers and magnitude of expected changes is essential in identifying system resilience and mitigation measures. We present a selection of state-of-the-art Neural Networks (NN) for the prediction of water temperatures in six streams in Germany.
  arXiv  Detail & Related papers  (2021-10-08T17:04:42Z)
• A Data Scientist's Guide to Streamflow Prediction [55.22219308265945]
  We focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow. This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way.
  arXiv  Detail & Related papers  (2020-06-05T08:04:37Z)

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.