A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks

Qiu, Minghui; Zhao, Peilin; Zhang, Ke; Huang, Jun; Shi, Xing; Wang, Xiaoguang; Chu, Wei

doi:10.1109/icdm.2017.49

Cited by 99 publications

(47 citation statements)

References 16 publications

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“…The former focuses on mining from the source labeled data to find those instances that are similar to the distribution of the target domain, and combine them together with the target labeled data [8,15]. The core idea of the latter line of work is to find a shared feature space, which can reduce the divergence between the distribution of the source and the target domains [1,17,28,33]. Our work follows the latter one, and tries to leverage NN models to learn a shared hidden representation for sentence pairs across domains.…”

Section: Extrinsic Evaluationsmentioning

confidence: 99%

Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

Qiu

Jiang

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications.In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva 1 in our E-commerce site AliExpress 2 .

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Section: Extrinsic Evaluationsmentioning

confidence: 99%

Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

Qiu

Jiang

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Self Cite

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“…MTL can learn tasks in parallel while using a shared representation and what is learned for each task can help other tasks be learned better (Caruana 1997). MTL is able to study the commonalities and differences between different tasks and has shown good performance in rainfall prediction (Qiu et al 2017), neuroimaging studies (Ma et al 2018) and railway track inspection (Gibert et al 2017).…”

Section: Introductionmentioning

confidence: 99%

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin

Zhong

Wang

et al. 2020

Environ. Res. Lett.

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Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE=0.82 Mg ha −1 ) as compared to that of conventional methods such as LASSO (RMSE=1.14 Mg ha −1 ) and Random Forest (RMSE=1.05 Mg ha −1 ). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions.

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“…Ref. [35] predicted rainfall at multiple sites simultaneously with multitask learning and [36] forecasted wind power ramp events using multitask deep neural networks.…”

Section: Neural Network For Signal Processingmentioning

confidence: 99%

Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks

Lee

Son

2020

Applied Sciences

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Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Research on temperature forecasting has taken one of two directions: time-series analysis and machine learning algorithms. Recently, a large amount of high-frequent climate data have been well-stored and become available. In this study, we apply three types of neural networks, multilayer perceptron, recurrent, and convolutional, to daily average, minimum, and maximum temperature forecasting with higher-frequency input features than researchers used in previous studies. Applying these neural networks to the observed data from three locations with different climate characteristics, we show that prediction performance with highly frequent hourly input data is better than forecasting performance with less-frequent daily inputs. We observe that a convolutional neural network, which has been mostly employed for processing satellite images rather than numeric weather data for temperature forecasting, outperforms the other models. In addition, we combine state of the art weather forecasting techniques with the convolutional neural network and evaluate their effects on the temperature forecasting performances.

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A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks

Cited by 99 publications

References 16 publications

Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks

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