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Currently, the world is in a period of urbanization that will accelerate the processes of land use cover and ecological change. Thus, establishing a land use and land cover change (LUCC) prediction and simulation model is of great significance for understanding the process of urban change and assessing its ecological impact. In previous studies, LUCC prediction models have been mainly based on cellular automata (CA) structures that calculate a future state pixel by pixel through transition rules. Because these transition rules are usually based on the global state and each pixel is calculated according to these fixed rules, the results of these methods have room for improvement in terms of generating details and heterogeneity. In this paper, a generative adversarial network (GAN)-based LUCC prediction model using multi-scale local spatial information is proposed. The model is based on a pix2pix GAN and an attention structure that predicts future land use through multi-scale local spatial information. To validate our model, Shenzhen, a region that is experiencing rapid urbanization was chosen as the source of the experimental data. The results indicate the proposed method achieved the highest accuracy in both short-time interval and long-time interval scenarios. In addition, the results of the proposed method were also closest to the ground truth from the perspective of the landscape pattern.
Shuting Sun; Ruyi Feng; Lin Mu; Lizhe Wang; Jijun He. GAN-Based LUCC Prediction via the Combination of Prior City Planning Information and Land Use Probability. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleShuting Sun, Ruyi Feng, Lin Mu, Lizhe Wang, Jijun He. GAN-Based LUCC Prediction via the Combination of Prior City Planning Information and Land Use Probability. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleShuting Sun; Ruyi Feng; Lin Mu; Lizhe Wang; Jijun He. 2021. "GAN-Based LUCC Prediction via the Combination of Prior City Planning Information and Land Use Probability." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
An important part of establishing a prediction model for maritime search and rescue and oil spill drift is to quantify the uncertainty in particle motion simulation. A regional Sub-grid velocity model based on drifting buoy data is proposed to simulate the unsolved velocity which is composed of turbulence and advection simulation errors (TASE) in the ocean model. Contrary to most of the traditional drift trajectory prediction models, the presented method divided the experimental area into 69 custom grids. The standard deviation of TASE velocity, TASE time scale, and TASE diffusion coefficients of each grid were obtained by using the fitting velocity difference of buoy velocity, current velocity and wind velocity, and autocorrelation analysis of time series. Two Sub-grid velocity models, the random flight model and the random walk model, were built respectively and were combined with the Lagrangian particle tracking model to simulate the drifting buoy trajectory. In the process of Lagrange particle tracking, fourth-order Runge-Kutta was used to interpolate the velocity and kernel density estimate method was used to calculate the predicted range of 95% confidence interval for evaluation and validation. The experimental results of several different settings indicated that the prediction performance of the random flight model was better than that of the random walk model when the TASE diffusion coefficient was determined by this method, and the prediction performance of models with different diffusion coefficients is better than that of models with the same diffusion coefficient. This work also proved the value of using the trajectory data of drifting buoys to build a Sub-grid velocity model for trajectory prediction.
Kui Zhu; Lin Mu; Xiaoyu Xia. An ensemble trajectory prediction model for maritime search and rescue and oil spill based on sub-grid velocity model. Ocean Engineering 2021, 236, 109513 .
AMA StyleKui Zhu, Lin Mu, Xiaoyu Xia. An ensemble trajectory prediction model for maritime search and rescue and oil spill based on sub-grid velocity model. Ocean Engineering. 2021; 236 ():109513.
Chicago/Turabian StyleKui Zhu; Lin Mu; Xiaoyu Xia. 2021. "An ensemble trajectory prediction model for maritime search and rescue and oil spill based on sub-grid velocity model." Ocean Engineering 236, no. : 109513.
Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder–decoder long short-term memory and Conv long short-term memory encoder–decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder–decoder gate recurrent unit model.
Si Ha; Darong Liu; Lin Mu. Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation. Scientific Reports 2021, 11, 1 -23.
AMA StyleSi Ha, Darong Liu, Lin Mu. Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation. Scientific Reports. 2021; 11 (1):1-23.
Chicago/Turabian StyleSi Ha; Darong Liu; Lin Mu. 2021. "Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation." Scientific Reports 11, no. 1: 1-23.
Amphidrome refers to an oceanic point where the amplitude of one harmonic constituent of the tidal system is zero, and the phase of the harmonic constituent is undetermined. The concept of amphidrome can also be used in climatological studies because of the existence of annual amphidromes, points of zero amplitude and ill‐defined phase of annual cycle. This study investigated the global atmospheric geopotential height from the ERA‐Interim to identify annual amphidromes at different isobaric altitudes. Many well‐defined annual amphidromes were identified. These amphidromic points appear always as twins with different rotating directions with respect to phases. This phenomenon can be explained mathematically with the basic theory of spherical algebraic topology, suggesting amphidrome twins as a common feature for periodic variables in the atmosphere. Due to the spatial continuity of atmospheric parameters, amphidromes at different isobaric altitudes can be concatenated into amphidromic lines. Amphidromic lines have a three‐dimensional structure with both clockwise and anticlockwise vertical branches connected by one or two horizontal components. Particularly, some amphidromic lines in the atmosphere can be closed loops when they are connected by two horizontal components. The rotary phases around the amphidromic loops are similar to the magnetic induction lines around a closed electromagnetic coil.
Enjin Zhao; Lin Mu; Haoyu Jiang. Amphidromic Lines in the Atmosphere: An Example of Global Pressure Field Annual Harmonic. Earth and Space Science 2021, 8, 1 .
AMA StyleEnjin Zhao, Lin Mu, Haoyu Jiang. Amphidromic Lines in the Atmosphere: An Example of Global Pressure Field Annual Harmonic. Earth and Space Science. 2021; 8 (4):1.
Chicago/Turabian StyleEnjin Zhao; Lin Mu; Haoyu Jiang. 2021. "Amphidromic Lines in the Atmosphere: An Example of Global Pressure Field Annual Harmonic." Earth and Space Science 8, no. 4: 1.
Storm surge is a natural disaster, often causing economic damage and loss of human life in the coastal communities. In recent decades, with more people attracted to coastal areas, the potential economic losses resulted from storm surges are increasing. Therefore, it is important to make risk assessments to identify areas at risk and design risk reduction strategies. However, the quantitative risk assessment of storm surge for coastal cities in China is often difficult due to the lack of adequate data regarding the building footprint and vulnerability curves. This paper aims to provide a methodology for conducting the quantitative risk assessment of storm surge, estimating direct tangible damage, by using Geographical Information System (GIS) techniques and open data. The proposed methodology was applied to a coastal area with a high concentration of petroleum industries in the Daya Bay zone. At first, five individual typhoon scenarios with different return periods (1000, 100, 50, 20, and 10 years) were defined. Then, the Advanced Circulation model and the Simulating Waves Nearshore model were utilized to simulate storm surge. The model outputs were imported into GIS software, transformed into inundation area and inundation depth. Subsequently, the building footprint data were extracted by the use of GIS techniques, including spatial analysis and image analysis. The layer containing building footprints was superimposed on the inundation area layer to identify and quantify the exposed elements to storm surge hazard. Combining the exposed elements with their related depth–damage functions, the quantitative risk assessment translates the spatial extent and depth of storm surge into the estimation of economic losses. The quantitative risk assessment and zonation maps for sub-zones in the study area can help local decision-makers to prioritize the sub-zones that are more likely to be affected by storm surge, make risk mitigation strategies, and develop long-term urban plans.
Si Wang; Lin Mu; Mengnan Qi; Zekun Yu; Zhenfeng Yao; Enjin Zhao. Quantitative risk assessment of storm surge using GIS techniques and open data: A case study of Daya Bay Zone, China. Journal of Environmental Management 2021, 289, 112514 .
AMA StyleSi Wang, Lin Mu, Mengnan Qi, Zekun Yu, Zhenfeng Yao, Enjin Zhao. Quantitative risk assessment of storm surge using GIS techniques and open data: A case study of Daya Bay Zone, China. Journal of Environmental Management. 2021; 289 ():112514.
Chicago/Turabian StyleSi Wang; Lin Mu; Mengnan Qi; Zekun Yu; Zhenfeng Yao; Enjin Zhao. 2021. "Quantitative risk assessment of storm surge using GIS techniques and open data: A case study of Daya Bay Zone, China." Journal of Environmental Management 289, no. : 112514.
Storm surge is one of the most destructive marine disasters to life and property for Chinese coastal regions, especially for Guangdong Province. In Huizhou city, Guangdong Province, due to the high concentrations of chemical and petroleum industries and the high population density, the low-lying coastal area is susceptible to the storm surge. Therefore, a comprehensive risk assessment of storm surge over the coastal area of Huizhou can delimit zones that could be affected to reduce disaster losses. In this paper, typhoon intensity for the minimum central pressure of 880, 910, 920, 930, and 940 hPa (corresponding to a 1000-, 100-, 50-, 20-, and 10-year return period) scenarios was designed to cover possible situations. The Jelesnianski method and the Advanced Circulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model were utilized to simulate inundation extents and depths of storm surge over the computational domain under these representative scenarios. Subsequently, the output data from the coupled simulation model (ADCIRC–SWAN) were imported to the geographic information system (GIS) software to conduct the hazard assessment for each of the designed scenarios. Then, the vulnerability assessment was made based on the dataset of land cover types in the coastal region. Consequently, the potential storm surge risk maps for the designed scenarios were produced by combining hazard assessment and vulnerability assessment with the risk matrix approach. The risk maps indicate that due to the protection given by storm surge barriers, only a small proportion of the petrochemical industrial zone and the densely populated communities in the coastal areas were at risk of storm surge for the scenarios of 10- and 20-year return period typhoon intensity. Moreover, some parts of the exposed zone and densely populated communities were subject to high and very high risk when typhoon intensities were set to a 50- or a 100-year return period. Besides, the scenario with the most intense typhoon (1000-year return period) induced a very high risk to the coastal area of Huizhou. Accordingly, the risk maps can help decision-makers to develop risk response plans and evacuation strategies in coastal communities with a high population density to minimize civilian casualties. The risk analysis can also be utilized to identify the risk zones with the high concentration of chemical and petroleum industries to reduce economic losses and prevent environmental damage caused by the chemical pollutants and oil spills from petroleum facilities and infrastructures that could be affected by storm surge.
Si Wang; Lin Mu; Zhenfeng Yao; Jia Gao; Enjin Zhao; Lizhe Wang. Assessing and zoning of typhoon storm surge risk with a geographic information system (GIS) technique: a case study of the coastal area of Huizhou. Natural Hazards and Earth System Sciences 2021, 21, 439 -462.
AMA StyleSi Wang, Lin Mu, Zhenfeng Yao, Jia Gao, Enjin Zhao, Lizhe Wang. Assessing and zoning of typhoon storm surge risk with a geographic information system (GIS) technique: a case study of the coastal area of Huizhou. Natural Hazards and Earth System Sciences. 2021; 21 (1):439-462.
Chicago/Turabian StyleSi Wang; Lin Mu; Zhenfeng Yao; Jia Gao; Enjin Zhao; Lizhe Wang. 2021. "Assessing and zoning of typhoon storm surge risk with a geographic information system (GIS) technique: a case study of the coastal area of Huizhou." Natural Hazards and Earth System Sciences 21, no. 1: 439-462.
Remote sensing building extraction is of great importance to many applications, such as urban planning and economic status assessment. Deep learning with deep network structures and back-propagation optimization can automatically learn features of targets in high-resolution remote sensing images. However, it is also obvious that the generalizability of deep networks is almost entirely dependent on the quality and quantity of the labels. Therefore, building extraction performances will be greatly affected if there is a large intra-class variation among samples of one class target. To solve the problem, a subdivision method for reducing intra-class differences is proposed to enhance semantic segmentation. We proposed that backgrounds and targets be separately generated by two orthogonal generative adversarial networks (O-GAN). The two O-GANs are connected by adding the new loss function to their discriminators. To better extract building features, drawing on the idea of fine-grained image classification, feature vectors for a target are obtained through an intermediate convolution layer of O-GAN with selective convolutional descriptor aggregation (SCDA). Subsequently, feature vectors are clustered into new, different subdivisions to train semantic segmentation networks. In the prediction stages, the subdivisions will be merged into one class. Experiments were conducted with remote sensing images of the Tibet area, where there are both tall buildings and herdsmen’s tents. The results indicate that, compared with direct semantic segmentation, the proposed subdivision method can make an improvement on accuracy of about 4%. Besides, statistics and visualizing building features validated the rationality of features and subdivisions.
Shuting Sun; Lin Mu; Lizhe Wang; Peng Liu; Xiaolei Liu; Yuwei Zhang. Semantic Segmentation for Buildings of Large Intra-Class Variation in Remote Sensing Images with O-GAN. Remote Sensing 2021, 13, 475 .
AMA StyleShuting Sun, Lin Mu, Lizhe Wang, Peng Liu, Xiaolei Liu, Yuwei Zhang. Semantic Segmentation for Buildings of Large Intra-Class Variation in Remote Sensing Images with O-GAN. Remote Sensing. 2021; 13 (3):475.
Chicago/Turabian StyleShuting Sun; Lin Mu; Lizhe Wang; Peng Liu; Xiaolei Liu; Yuwei Zhang. 2021. "Semantic Segmentation for Buildings of Large Intra-Class Variation in Remote Sensing Images with O-GAN." Remote Sensing 13, no. 3: 475.
El Niño can affect climate patterns, causing extreme weather events, such as floods and droughts, around the world. Accurate forecasting of El Niño events allows preparation for El Niño-related disasters. However, the performance of current methods for predicting El Niño events one year in advance is not effective. This study proposes a hybrid approach to predicting the El Niño-related Oceanic Niño Index (ONI) and El Niño events with a lead time of 12 months. The proposed approach combines the convolutional Long Short-Term Memory (LSTM) Encoder-Decoder model with the Empirical Mode Decomposition (EMD) technique. The EMD technique can decompose time series data into a set of Intrinsic Mode Functions and a residue. Subsequently, the convolutional LSTM Encoder-Decoder model is employed to make an independent prediction for each component. Finally, the predicted data from each model can be reconstructed to obtain the forecasting results. The proposed approach is applied to the monthly ONI dataset for 1950–2019. The prediction model is trained and validated on the historical ONI values from 1950 to 2007 and forecasts El Niño events over a period of 12 years (2008–2019) with a lead time of 12 months. The results demonstrate that the proposed approach can successfully forecast that, for this period, 2009–2010, 2015–2016, and 2018–2019 are El Niño years. The performance of the proposed approach is then assessed by comparing it with the standalone convolutional LSTM Encoder-Decoder model, the LSTM-based models, and machine learning algorithms. The evaluated results indicate that the proposed approach outperforms these models in ONI predictions and El Niño event forecasts for a lead time of 12 months.
Si Wang; Lin Mu; Darong Liu. A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder. Computers & Geosciences 2021, 149, 104695 .
AMA StyleSi Wang, Lin Mu, Darong Liu. A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder. Computers & Geosciences. 2021; 149 ():104695.
Chicago/Turabian StyleSi Wang; Lin Mu; Darong Liu. 2021. "A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder." Computers & Geosciences 149, no. : 104695.
Refined estimation of bottom friction coefficient (BFC) is significantly important for coastal engineers to determine precisely the hydrodynamic conditions and sediment transport rates, which can be realized by assimilating observations of sea surface elevation using the adjoint method. In this study, a new method, named as feature point scheme (FPS), is developed to further improve the estimation of temporally varying BFC. In FPS, BFCs at some feature points are assumed to be independent and BFCs at other temporal points are obtained by interpolating BFCs at feature points. By assimilating artificial observations in twin experiments, it is demonstrated that FPS can improve the estimated temporally varying BFC, especially with the nonuniformly distributed feature points and nonuniform spline interpolation (nonuniform FPS). The M2 tide and four principal tidal constituents in the Bohai Sea are then simulated by assimilating real satellite-retrieved observations with FPS. In all the experiments, the performance of the tidal model with nonuniform FPS outperforms those using other schemes. The experimental results indicate that the temporally varying BFC is related to the tidal constituents. Moreover, the temporal variation of estimated BFC has a significantly negative correlation with the current speed, which is possibly related with the sediment transport.
Daosheng Wang; Jicai Zhang; Lin Mu. A feature point scheme for improving estimation of the temporally varying bottom friction coefficient in tidal models using adjoint method. Ocean Engineering 2020, 220, 108481 .
AMA StyleDaosheng Wang, Jicai Zhang, Lin Mu. A feature point scheme for improving estimation of the temporally varying bottom friction coefficient in tidal models using adjoint method. Ocean Engineering. 2020; 220 ():108481.
Chicago/Turabian StyleDaosheng Wang; Jicai Zhang; Lin Mu. 2020. "A feature point scheme for improving estimation of the temporally varying bottom friction coefficient in tidal models using adjoint method." Ocean Engineering 220, no. : 108481.
Change detection of high-resolution remote sensing images is an important task in earth observation and was extensively investigated. Recently, deep learning has shown to be very successful in plenty of remote sensing tasks. The current deep learning-based change detection method is mainly based on conventional long short-term memory (Conv-LSTM), which does not have spatial characteristics. Since change detection is a process with both spatiality and temporality, it is necessary to propose an end-to-end spatiotemporal network. To achieve this, Conv-LSTM, an extension of the Conv-LSTM structure, is introduced. Since it shares similar spatial characteristics with the convolutional layer, L-UNet, which substitutes partial convolution layers of UNet-to-Conv-LSTM and Atrous L-UNet (AL-UNet), which further using Atrous structure to multiscale spatial information is proposed. Experiments on two data sets are conducted and the proposed methods show the advantages both in quantity and quality when compared with some other methods.
Shuting Sun; Lin Mu; Lizhe Wang; Peng Liu. L-UNet: An LSTM Network for Remote Sensing Image Change Detection. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleShuting Sun, Lin Mu, Lizhe Wang, Peng Liu. L-UNet: An LSTM Network for Remote Sensing Image Change Detection. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleShuting Sun; Lin Mu; Lizhe Wang; Peng Liu. 2020. "L-UNet: An LSTM Network for Remote Sensing Image Change Detection." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
In this paper, an experimental study is presented to investigate the effect of improved heat and mass transfer by using lenses and reflector on the solar still's thermodynamic processes. This study used two identical solar stills with the basin area of 1 m2: one is conventional solar still (SS) and the other one is a new solar still (NSS) equipped with lenses and mirror. The experimental results showed that the average Nusselt and Grashof numbers inside NSS cavity were enhanced by about 50% and 125% respectively, compared to SS. The maximum output per hour of NSS was 0.437 L/(m2·hr), and a new thermal model for convective mass transfer in solar still was proposed and established, and the modified values of C and n for Nu = C(GrPr)n were proposed as C = 0.9, n = 0.175 for 4.65 × 107 < Gr < 3.07 × 108. The agreement between the correlations and experimental results was fairly good with an error of about 10%. However, there were deviations between theoretical values and experimental values for Dunkle Model (base on C = 0.075, n = 1/3), and Kumar & Tiwari Model (base on C = 0.0322, n = 0.4114). The maximum value of evaporative heat transfer coefficient predicted by Kumar & Tiwari Model was 218.51% higher than that of the new thermal model in this research. The maximum value of evaporative heat transfer coefficient predicted by Dunkle Model was 137.33% higher than that of the new thermal model. After the performance assessment, it was found that NSS is more effective than the conventional type, and the price of 1 L of fresh water equals 0.305 RMB for this kind solar water recovery device.
Shibiao Fang; Lin Mu; Wenrong Tu. Heat and mass transfer analysis in a solar water recovery device: Experimental and theoretical distillate output study. Desalination 2020, 500, 114881 .
AMA StyleShibiao Fang, Lin Mu, Wenrong Tu. Heat and mass transfer analysis in a solar water recovery device: Experimental and theoretical distillate output study. Desalination. 2020; 500 ():114881.
Chicago/Turabian StyleShibiao Fang; Lin Mu; Wenrong Tu. 2020. "Heat and mass transfer analysis in a solar water recovery device: Experimental and theoretical distillate output study." Desalination 500, no. : 114881.
In this paper, a novel small-decentralized desalination device is presented for increasing the temperature difference between brackish water and glass cover, so as to enhance the solar still’s freshwater productivity. This novel solar still device is based on installing a lens in front of the single slope solar still, and two lenses on still’s both sides, and a reflector over the back side of the solar still. The main objective of such novel still is to concentrate sunrays at the still’s bottom basin, through the refraction function of Fresnel lens. Furthermore, this novel still makes the reflector transferring its reflected sunrays to the solar still’s basin. Experiments are conducted under the climate conditions in Hangzhou city, China, for testing the novel still’s operational performance, and internal heat and mass transfer characteristics. Assessment of the novel still’s feasibility is performed based on energy, exergy, exergoeconomic, and enviroeconomic methodologies, as well as energy payback time. Results show that the productivity of novel still is 32% higher than that of conventional still, and novel solar still enhances the average hourly energy efficiency by 97.73%, compared to conventional solar still. While, the corresponding value of hourly exergy efficiency is also enhanced by 43.713%. Due to the higher energy and exergy outputs of novel still throughout its lifetime, the novel solar still proposed in this study mitigates more CO2 compared to the conventional still. Overall, incorporation three lenses and one reflector with the still is found promising in terms of freshwater yield, cost, and energy payback time compared to conventional one. Exergoeconomic and environmental parameters of the novel solar still are found more effective compared to the conventional one.
Shibiao Fang; Lin Mu; Wenrong Tu. Application design and assessment of a novel small-decentralized solar distillation device based on energy, exergy, exergoeconomic, and enviroeconomic parameters. Renewable Energy 2020, 164, 1350 -1363.
AMA StyleShibiao Fang, Lin Mu, Wenrong Tu. Application design and assessment of a novel small-decentralized solar distillation device based on energy, exergy, exergoeconomic, and enviroeconomic parameters. Renewable Energy. 2020; 164 ():1350-1363.
Chicago/Turabian StyleShibiao Fang; Lin Mu; Wenrong Tu. 2020. "Application design and assessment of a novel small-decentralized solar distillation device based on energy, exergy, exergoeconomic, and enviroeconomic parameters." Renewable Energy 164, no. : 1350-1363.
El Niño and Southern Oscillation (ENSO) events are usually monitored by tropical Pacific sea surface temperature anomaly (SSTA) patterns with dramatic impacts on the global climate. To explore the diversity of the tropical Pacific SSTA, a novel method combined empirical orthogonal function (EOF) analysis and the K‐means clustering algorithm is carried out to classify SSTA patterns during 1950‐2016. Meanwhile, the total distance variance and total silhouette value are introduced to determine an optimal number of distinguishable representative SSTA patterns. Ten SSTA patterns are obtained, which shows frequent basin‐wide warming and extreme cold ENSO events in recent decades. It may be attributed to the changes in composition of the intrinsic modes along with the background mode of slowly increasing east‐west SST gradient. The comparative analysis between periods 1950‐1969 and 1997‐2016 suggests that the two regimes of tropical Pacific SSTA, featured as extreme warm and moderate warm/extreme cold patterns respectively, become more distinct under recent global warming. This article is protected by copyright. All rights reserved.
Jing Li; Lin Mu; Linhao Zhong. Distinct tropical Pacific sea surface temperature anomaly regimes enhanced under recent global warming. International Journal of Climatology 2020, 41, 970 -979.
AMA StyleJing Li, Lin Mu, Linhao Zhong. Distinct tropical Pacific sea surface temperature anomaly regimes enhanced under recent global warming. International Journal of Climatology. 2020; 41 (2):970-979.
Chicago/Turabian StyleJing Li; Lin Mu; Linhao Zhong. 2020. "Distinct tropical Pacific sea surface temperature anomaly regimes enhanced under recent global warming." International Journal of Climatology 41, no. 2: 970-979.
Storm surge is one of the most destructive marine disasters to life and property for Chinese coastal regions, especially for Guangdong province. In Huizhou city, Guangdong province, due to the high concentration of chemical and petroleum industries and the high population density, the low-lying coastal area is susceptible to the storm surge. Therefore, a comprehensive risk assessment of storm surge over the coastal area of Huizhou can delimit zones that could be affected to reduce disaster losses. In this paper, typhoon intensity for the minimum central pressure of 880 hPa, 910 hPa, 920 hPa, 930 hPa, and 940 hPa (corresponding to 1000-year, 100-year, 50-year, 20-year, and 10-year return period) scenarios were designed to cover possible situations. The Jelesnianski method and the Advanced Circulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model were utilized to simulate inundation extents and depths of storm surge over the computational domain under these representative scenarios. Subsequently, the output data from the coupled simulation model (ADCIRC–SWAN) were imported to Geographical Information System (GIS) software to conduct the hazard assessment for each of the designed scenarios. Then, the vulnerability assessment was made based on the dataset of land cover types in the coastal region. Consequently, the potential storm surge risk maps for the designed scenarios were produced by combining hazard assessment and vulnerability assessment with the risk matrix approach. The risk maps indicate that due to the protection given by storm surge barriers, only a small proportion of the petrochemical industrial zone and the densely populated communities in the coastal areas were at risk of storm surge for the scenarios of 10-year and 20-year return period typhoon intensity. Moreover, some parts of the exposed zone and densely populated communities were subject to high and very high risk when typhoon intensities were set to a 50-year or a 100-year return period. Besides, the scenario with the most intense typhoon (1000-year return period) induced the very high risk to the coastal area of Huizhou. Accordingly, the risk maps can help decision-makers to develop risk response plans and evacuation strategies in coastal communities with the high population density to minimize civilian casualties. The risk analysis can also be utilized to identify the risk zones with the high concentration of chemical and petroleum industries to reduce economic losses and prevent environmental damage caused by the chemical pollutants and oil spills from petroleum facilities and infrastructures that could be affected by storm surge.
Si Wang; Lin Mu; Zhenfeng Yao; Jia Gao; Enjin Zhao. Assessing and zoning of typhoon storm surge risk with GIS technique: A case study of the coastal area of Huizhou. 2020, 2020, 1 -35.
AMA StyleSi Wang, Lin Mu, Zhenfeng Yao, Jia Gao, Enjin Zhao. Assessing and zoning of typhoon storm surge risk with GIS technique: A case study of the coastal area of Huizhou. . 2020; 2020 ():1-35.
Chicago/Turabian StyleSi Wang; Lin Mu; Zhenfeng Yao; Jia Gao; Enjin Zhao. 2020. "Assessing and zoning of typhoon storm surge risk with GIS technique: A case study of the coastal area of Huizhou." 2020, no. : 1-35.
El Niño-Southern Oscillation (ENSO), which is one of the main drivers of Earth’s inter-annual climate variability, often causes a wide range of climate anomalies, and the advance prediction of ENSO is always an important and challenging scientific issue. Since a unified and complete ENSO theory has yet to be established, people often use related indicators, such as the Niño 3.4 index and southern oscillation index (SOI), to predict the development trends of ENSO through appropriate numerical simulation models. However, because the ENSO phenomenon is a highly complex and dynamic model and the Niño 3.4 index and SOI mix many low- and high-frequency components, the prediction accuracy of current popular numerical prediction methods is not high. Therefore, this paper proposed the ensemble empirical mode decomposition-temporal convolutional network (EEMD-TCN) hybrid approach, which decomposes the highly variable Niño 3.4 index and SOI into relatively flat subcomponents and then uses the TCN model to predict each subcomponent in advance, finally combining the sub-prediction results to obtain the final ENSO prediction results. Niño 3.4 index and SOI reanalysis data from 1871 to 1973 were used for model training, and the data for 1984–2019 were predicted 1 month, 3 months, 6 months, and 12 months in advance. The results show that the accuracy of the 1-month-lead Niño 3.4 index prediction was the highest, the 12-month-lead SOI prediction was the slowest, and the correlation coefficient between the worst SOI prediction result and the actual value reached 0.6406. Furthermore, the overall prediction accuracy on the Niño 3.4 index was better than that on the SOI, which may have occurred because the SOI contains too many high-frequency components, making prediction difficult. The results of comparative experiments with the TCN, LSTM, and EEMD-LSTM methods showed that the EEMD-TCN provides the best overall prediction of both the Niño 3.4 index and SOI in the 1-, 3-, 6-, and 12-month-lead predictions among all the methods considered. This result means that the TCN approach performs well in the advance prediction of ENSO and will be of great guiding significance in studying it.
Jining Yan; Lin Mu; Lizhe Wang; Rajiv Ranjan; Albert Y. Zomaya. Temporal Convolutional Networks for the Advance Prediction of ENSO. Scientific Reports 2020, 10, 1 -15.
AMA StyleJining Yan, Lin Mu, Lizhe Wang, Rajiv Ranjan, Albert Y. Zomaya. Temporal Convolutional Networks for the Advance Prediction of ENSO. Scientific Reports. 2020; 10 (1):1-15.
Chicago/Turabian StyleJining Yan; Lin Mu; Lizhe Wang; Rajiv Ranjan; Albert Y. Zomaya. 2020. "Temporal Convolutional Networks for the Advance Prediction of ENSO." Scientific Reports 10, no. 1: 1-15.
Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are inter-correlated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-network-based correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the root-mean-square error between the U10 from altimeters and buoys was reduced from 1.45 m/s to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.
Haoyu Jiang; Hao Zheng; Lin Mu. Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 1917 -1924.
AMA StyleHaoyu Jiang, Hao Zheng, Lin Mu. Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):1917-1924.
Chicago/Turabian StyleHaoyu Jiang; Hao Zheng; Lin Mu. 2020. "Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 1917-1924.
The most important motivation for streamflow forecasts is flood prediction and longtime continuous prediction in hydrological research. As for many traditional statistical models, forecasting flood peak discharge is nearly impossible. They can only get acceptable results in normal year. On the other hand, the numerical methods including physics mechanisms and rainfall-atmospherics could provide a better performance when floods coming, but the minima prediction period of them is about one month ahead, which is too short to be used in hydrological application. In this study, a deep neural network was employed to predict the streamflow of the Hankou Hydrological Station on the Yangtze River. This method combined the Empirical Mode Decomposition (EMD) algorithm and Encoder Decoder Long Short-Term Memory (En-De-LSTM) architecture. Owing to the hydrological series prediction problem usually contains several different frequency components, which will affect the precision of the longtime prediction. The EMD technique could read and decomposes the original data into several different frequency components. It will help the model to make longtime predictions more efficiently. The LSTM based En-De-LSTM neural network could make the forecasting closer to the observed in peak flow value through reading, training, remembering the valuable information and forgetting the useless data. Monthly streamflow data (from January 1952 to December 2008) from Hankou Hydrological Station on the Yangtze River was selected to train the model, and predictions were made in two years with catastrophic flood events and ten years rolling forecast. Furthermore, the Root Mean Square Error (RMSE), Coefficient of Determination (R2), Willmott’s Index of agreement (WI) and the Legates-McCabe’s Index (LMI) were used to evaluate the goodness-of-fit and performance of this model. The results showed the reliability of this method in catastrophic flood years and longtime continuous rolling forecasting.
Darong Liu; Wenchao Jiang; Lin Mu; Si Wang. Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River. IEEE Access 2020, 8, 90069 -90086.
AMA StyleDarong Liu, Wenchao Jiang, Lin Mu, Si Wang. Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River. IEEE Access. 2020; 8 (99):90069-90086.
Chicago/Turabian StyleDarong Liu; Wenchao Jiang; Lin Mu; Si Wang. 2020. "Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River." IEEE Access 8, no. 99: 90069-90086.
The ability of separating fresh water from saline liquid is significant to indicate the performance of evaporator device. To reveal the physical mechanism of gas-liquid separation, this study experimentally tested the temperature change and evaporation rate in a gravity-based evaporator. Differential equations of heat and mass transfer were established for the phenomenon of high-pressure temperature-saturated water entered the low-pressure evaporator. Then heat-transfer and mass-transfer coefficients were figured out, and an initial analogy exploration of heat and mass transfer resulted in a coefficient of determination of 0.96. As such, variables used to describe heat and mass transfer were analyzed based on Buckingham's-π theorem, determining three new dimensionless numbers. Relationship between heat and mass transfer inside evaporator was established by using partial experimental data to perform multiple regression on logarithmic dimensionless variables. Finally, formula of modified Chilton-Colburn analogy for such relationship was verified using another part of the experimental data, with a calculation error 72 °C) evaporation with drastic change of humidity accompanied by excess steam condensation.
Shibiao Fang; Lin Mu. Heat and mass transfer analysis during gas-liquid separation in an evaporator device for desalination. Desalination 2020, 486, 114430 .
AMA StyleShibiao Fang, Lin Mu. Heat and mass transfer analysis during gas-liquid separation in an evaporator device for desalination. Desalination. 2020; 486 ():114430.
Chicago/Turabian StyleShibiao Fang; Lin Mu. 2020. "Heat and mass transfer analysis during gas-liquid separation in an evaporator device for desalination." Desalination 486, no. : 114430.
The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.
Linhao Zhong; Lin Mu; Jing Li; Jiaying Wang; Zhe Yin; Darong Liu. Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model. IEEE Access 2020, 8, 51761 -51769.
AMA StyleLinhao Zhong, Lin Mu, Jing Li, Jiaying Wang, Zhe Yin, Darong Liu. Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model. IEEE Access. 2020; 8 (99):51761-51769.
Chicago/Turabian StyleLinhao Zhong; Lin Mu; Jing Li; Jiaying Wang; Zhe Yin; Darong Liu. 2020. "Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model." IEEE Access 8, no. 99: 51761-51769.
Due to the strong interaction between the bridges and the extreme waves generated by hurricanes and tsunamis, many coastal bridges were damaged in the past few decades. In this study, in order to investigate the effect of the extreme wave on a bridge, tsunami-like wave generated based on record in the Iwate station during 2011 Japan tsunami event instead of solitary wave is employed to impinge on the bridge with or without air vent. According to the Computational Fluid Dynamics (CFD) theory with a Volume of Fluid (VOF) interface tracking approach, a Numerical Wave Tank (NWT) is developed, in which the Immersed Boundary (IB) method is referred to simulate fluid and structure interaction. The model is calibrated with the experiment. In this numerical tank, the effects of tsunami-like waves with different prominent conditions including wave height and submersion depth on the bridges are studied. Besides, the efficiency of air vent in reducing the hydrodynamic load is also discussed. The results indicate that when the tsunami-like wave overtops the bridges, a noticeable overtopping, where the flow injects into the water fiercely and the whole flow field is very chaotic, is observed. The vertical forces on the bridges are larger than the horizontal forces, leading to many decks to collapse between two piers with small lateral displacement after the hurricanes and tsunamis. After the tsunami-like wave passes over the bridge, the oscillation of the residual wave causes the load oscillation of the bridge. The position of the bridge relative to the initial sea level has a serious impact on the forces on the bridge when the tsunami-like wave passes through it. With the increase of the wave height, the interaction intensity between the tsunami-like wave and the bridge is enhanced and the forces on the bridge also increase, but the effect duration reduces. After the air vent is installed, air vent can reduce the forces on the bridge and improve the efficiency of the bridge protection.
Enjin Zhao; Junkai Sun; Yuezhao Tang; Lin Mu; Haoyu Jiang. Numerical investigation of tsunami wave impacts on different coastal bridge decks using immersed boundary method. Ocean Engineering 2020, 201, 107132 .
AMA StyleEnjin Zhao, Junkai Sun, Yuezhao Tang, Lin Mu, Haoyu Jiang. Numerical investigation of tsunami wave impacts on different coastal bridge decks using immersed boundary method. Ocean Engineering. 2020; 201 ():107132.
Chicago/Turabian StyleEnjin Zhao; Junkai Sun; Yuezhao Tang; Lin Mu; Haoyu Jiang. 2020. "Numerical investigation of tsunami wave impacts on different coastal bridge decks using immersed boundary method." Ocean Engineering 201, no. : 107132.