59,803 research outputs found
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Computational complexity has been the bottleneck of applying physically-based
simulations on large urban areas with high spatial resolution for efficient and
systematic flooding analyses and risk assessments. To address this issue of
long computational time, this paper proposes that the prediction of maximum
water depth rasters can be considered as an image-to-image translation problem
where the results are generated from input elevation rasters using the
information learned from data rather than by conducting simulations, which can
significantly accelerate the prediction process. The proposed approach was
implemented by a deep convolutional neural network trained on flood simulation
data of 18 designed hyetographs on three selected catchments. Multiple tests
with both designed and real rainfall events were performed and the results show
that the flood predictions by neural network uses only 0.5 % of time comparing
with physically-based approaches, with promising accuracy and ability of
generalizations. The proposed neural network can also potentially be applied to
different but relevant problems including flood predictions for urban layout
planning
Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method
for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch
of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP),
through collaborative operation of sewer system facilities. Using a hydraulic
model, the effectiveness of ICWT is investigated in a sewer system in Drammen,
Norway. Concerning the whole system performance, we found that the S{\o}ren
Lemmich pump station plays a vital role in the ICWT framework. To enhance the
operation of this pump station, it is imperative to construct a multi-step
ahead water level prediction model. Hence, one of the most promising artificial
intelligence techniques, Long Short Term Memory (LSTM), is employed to
undertake this task. Experiments demonstrated that LSTM is superior to Gated
Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural
Network (FFNN) and Support Vector Regression (SVR)
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
Smart City Development with Urban Transfer Learning
Nowadays, the smart city development levels of different cities are still
unbalanced. For a large number of cities which just started development, the
governments will face a critical cold-start problem: 'how to develop a new
smart city service with limited data?'. To address this problem, transfer
learning can be leveraged to accelerate the smart city development, which we
term the urban transfer learning paradigm. This article investigates the common
process of urban transfer learning, aiming to provide city planners and
relevant practitioners with guidelines on how to apply this novel learning
paradigm. Our guidelines include common transfer strategies to take, general
steps to follow, and case studies in public safety, transportation management,
etc. We also summarize a few research opportunities and expect this article can
attract more researchers to study urban transfer learning
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