1,096 research outputs found
Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan
Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated
Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast
Tropical cyclone (TC) is an extreme tropical weather system and its
trajectory can be described by a variety of spatio-temporal data. Effective
mining of these data is the key to accurate TCs track forecasting. However,
existing methods face the problem that the model complexity is too high or it
is difficult to efficiently extract features from multi-modal data. In this
paper, we propose the Dual-Branched spatio-temporal Fusion Network (DBF-Net) --
a novel multi-horizon tropical cyclone track forecasting model which fuses the
multi-modal features efficiently. DBF-Net contains a TC features branch that
extracts temporal features from 1D inherent features of TCs and a pressure
field branch that extracts spatio-temporal features from reanalysis 2D pressure
field. Through the encoder-decoder-based architecture and efficient feature
fusion, DBF-Net can fully mine the information of the two types of data, and
achieve good TCs track prediction results. Extensive experiments on historical
TCs track data in the Northwest Pacific show that our DBF-Net achieves
significant improvement compared with existing statistical and deep learning
TCs track forecast methods
Typhoon Intensity Prediction with Vision Transformer
Predicting typhoon intensity accurately across space and time is crucial for
issuing timely disaster warnings and facilitating emergency response. This has
vast potential for minimizing life losses and property damages as well as
reducing economic and environmental impacts. Leveraging satellite imagery for
scenario analysis is effective but also introduces additional challenges due to
the complex relations among clouds and the highly dynamic context. Existing
deep learning methods in this domain rely on convolutional neural networks
(CNNs), which suffer from limited per-layer receptive fields. This limitation
hinders their ability to capture long-range dependencies and global contextual
knowledge during inference. In response, we introduce a novel approach, namely
"Typhoon Intensity Transformer" (Tint), which leverages self-attention
mechanisms with global receptive fields per layer. Tint adopts a
sequence-to-sequence feature representation learning perspective. It begins by
cutting a given satellite image into a sequence of patches and recursively
employs self-attention operations to extract both local and global contextual
relations between all patch pairs simultaneously, thereby enhancing per-patch
feature representation learning. Extensive experiments on a publicly available
typhoon benchmark validate the efficacy of Tint in comparison with both
state-of-the-art deep learning and conventional meteorological methods. Our
code is available at https://github.com/chen-huanxin/Tint.Comment: 8 pages, 2 figures, accepted by Tackling Climate Change with Machine
Learning: workshop at NeurIPS 202
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean
Utilization Of Artificial Intelligence (AI) And Machine Learning (ML) in the Field of Energy Research
Many governments have committed to becoming carbon neutral by 2050. The main argument is that renewable resources are more eco-friendly than fossil fuels. However, the unpredictable nature of solar and wind power results in either excess or lack of energy generation. This article will evaluate the current machine-learning-based solutions for forecasting renewable energy demand and capacity. Many researchers have used machine learning (ML) to anticipate the amount of generated wind or solar energy. SVM, RNN, NN, and ELM are the most utilized algorithms. Prediction accuracy is improved through optimization (metaheuristics and evolution). These methods can forecast renewable energy for periods ranging from seconds to months. This article compares several ML methodologies and metaheuristic strategies and reviews the current state of research. The hybrid MLS outperforms the standalone optimizers. A more extensive data set for ANN, the introduction of NWP, and a shorter prediction timeframe are suggested as alternatives to Bayesian and random grid tuning. Further research on probabilistic predictions and mathematical relationships between inputs and outputs is needed to close the research gap
CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis
Convolutional neural networks (CNN) have achieved great success in analyzing
tropical cyclones (TC) with satellite images in several tasks, such as TC
intensity estimation. In contrast, TC structure, which is conventionally
described by a few parameters estimated subjectively by meteorology
specialists, is still hard to be profiled objectively and routinely. This study
applies CNN on satellite images to create the entire TC structure profiles,
covering all the structural parameters. By utilizing the meteorological domain
knowledge to construct TC wind profiles based on historical structure
parameters, we provide valuable labels for training in our newly released
benchmark dataset. With such a dataset, we hope to attract more attention to
this crucial issue among data scientists. Meanwhile, a baseline is established
with a specialized convolutional model operating on polar-coordinates. We
discovered that it is more feasible and physically reasonable to extract
structural information on polar-coordinates, instead of Cartesian coordinates,
according to a TC's rotational and spiral natures. Experimental results on the
released benchmark dataset verified the robustness of the proposed model and
demonstrated the potential for applying deep learning techniques for this
barely developed yet important topic.Comment: Submitted to AAAI202
Deep Learning Techniques in Extreme Weather Events: A Review
Extreme weather events pose significant challenges, thereby demanding
techniques for accurate analysis and precise forecasting to mitigate its
impact. In recent years, deep learning techniques have emerged as a promising
approach for weather forecasting and understanding the dynamics of extreme
weather events. This review aims to provide a comprehensive overview of the
state-of-the-art deep learning in the field. We explore the utilization of deep
learning architectures, across various aspects of weather prediction such as
thunderstorm, lightning, precipitation, drought, heatwave, cold waves and
tropical cyclones. We highlight the potential of deep learning, such as its
ability to capture complex patterns and non-linear relationships. Additionally,
we discuss the limitations of current approaches and highlight future
directions for advancements in the field of meteorology. The insights gained
from this systematic review are crucial for the scientific community to make
informed decisions and mitigate the impacts of extreme weather events
A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula
The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers
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