Tianjie Lei

A multi-core CPU and many-core GPU based fast parallel shuffled complex evolution global optimization approach

In the field of hydrological modelling, the global and automatic parameter calibration has been a hot issue for many years. Among automatic parameter optimization algorithms, the shuffled complex evolution developed at the University of Arizona (SCE-UA) is the most successful method for stably and robustly locating the global “best” parameter values. Ever since the invention of the SCE-UA, the profession suddenly has a consistent way to calibrate watershed models. However, the computational efficiency of the SCE-UA significantly deteriorates when coping with big data and complex models. For the purpose of solving the efficiency problem, the recently emerging heterogeneous parallel computing (parallel computing by using the multi-core CPU and many-core GPU) was applied in the parallelization and acceleration of the SCE-UA. The original serial and proposed parallel SCE-UA were compared to test the performance based on the Griewank benchmark function. The comparison results indicated that the parallel SCE-UA converged much faster than the serial version and its optimization accuracy was the same as the serial version. It has a promising application prospect in the field of fast hydrological model parameter optimization.

A novel hybrid data-driven model for multi-input single-output system simulation

Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks.

An improved hybrid data-driven model and its application in daily rainfall-runoff simulation

In previous literatures, a coupled data-driven rainfall-runoff (RR) model, NU-PEK, has been proposed and successfully applied in hourly RR simulation task. However, numerical experiments show that its performance for daily RR simulation is unsatisfactory. It is noticed that the poor performance is due to the inability of the original model to capture the much higher non-linear characteristics contained in the daily data. In order to improve the nonlinearity simulation capability of the original model, an improved model named NU-PKEK and its calibration methodology are developed in this paper. The improved model is constituted by adding a K-means clustering module and utilizing multiple NU-PEK modules instead of using only one NU-PEK model. This study applies the improved model, the Xinanjiang model, and the original model for daily RR simulation in Chengcun catchment for intercomparison and verification. The simulation results prove that the NU-PKEK performs best, and has better simulation and forecasting capability.

Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method

ABSTRACT Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall–runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.

The application of UAV remote sensing in mapping of damaged buildings after earthquakes

UAV (unmanned aerial vehicle) remote sensing system has advantages of strong real-time, flexible and convenient, little influence by the external environment, and the ability to work full-time. It can go deep into the places safely and reliably which staff can hardly arrived. The remote sensing system can be in response to emergencies to gain first-hand information as quickly as possible and have produced a unique emergency response to acquire an important basis for overall decision-making. However, UAV remote sensing system was so fast, flexible, low flying to carry on quick response to acquire high-resolution images. In the Wenchuan Earthquake, UAV remote sensing was applied successfully to acquire first-hand earthquake damage information in the short time under cloudy and rainy conditions of Sichuan Province. The system flow of UAV remote sensing to extract information on damaged houses after earthquake was set up successfully. Moreover, UAV remote sensing had an important role in mapping of damaged buildings after earthquake. Rapid identification of mapping of damaged buildings after earthquake with UAV remote sensing techniques can be carried out. UAV remote sensing techniques could have greater potentials for disaster mitigation and management after earthquake.

Water area changes of the Tonle Sap Lake based on remote sensing data

The Tonle Sap Lake plays a very important role in regulating the downstream flood of the Mekong River. It is necessary to understand its temporal changes of water area of the lake and to analyze its relation with the flood processes of the Mekong River. Monthly water area from June 2013 to May 2014 were monitored based on the multi-temporal images of HJ-1 satellite in this paper. Normalized difference water index (NDWI) was used to extract the water area. It is found that the water area of the lake had a dramatic increase from September to December. Moreover, after reaching its maximum in December 2013, the water area quickly decreased by 11463km2 in only half month time from December to January. It kept rather stable at a lower level from February to May in 2014. It is feasible, fast and reliable to monitor and analyze the change of lake water area based on remote sensing method with important application prospect.

The application of unmanned aerial vehicle remote sensing for monitoring secondary geological disasters after earthquakes

Remote sensing system fitted on Unmanned Aerial Vehicle (UAV) can obtain clear images and high-resolution aerial photographs. It has advantages of strong real-time, flexibility and convenience, free from influence of external environment, low cost, low-flying under clouds and ability to work full-time. When an earthquake happened, it could go deep into the places safely and reliably which human staff can hardly approach, such as secondary geological disasters hit areas. The system can be timely precise in response to secondary geological disasters monitoring by a way of obtaining first-hand information as quickly as possible, producing a unique emergency response capacity to provide a scientific basis for overall decision-making processes. It can greatly enhance the capability of on-site disaster emergency working team in data collection and transmission. The great advantages of UAV remote sensing system played an irreplaceable role in monitoring secondary geological disaster dynamics and influences. Taking the landslides and barrier lakes for example, the paper explored the basic application and process of UAV remote sensing in the disaster emergency relief. UAV high-resolution remote sensing images had been exploited to estimate the situation of disaster-hit areas and monitor secondary geological disasters rapidly, systematically and continuously. Furthermore, a rapid quantitative assessment on the distribution and size of landslides and barrier lakes was carried out. Monitoring results could support relevant government departments and rescue teams, providing detailed and reliable scientific evidence for disaster relief and decision-making.

Ecological benefits assessment of river flow diversion for Ejina oasis in Heihe River basin using remote sensing

A trans-provincial river flow diversion project has been implemented in the Heihe River basin since 2000 to stop the further deterioration of its downstream Ejina natural oasis. An emergent three-year water saving project were was also carried out during 2001 to 2003 as to prevent the downstream Ejina oasis from further withering. In this paper, desertification degree and net primary productivity (NPP) of the oasis based on remote sensing was used to assess the vegetation response of this natural oasis to the river flow diversion. Desertification was degraded with no significant expansion and natural oasis ecosystem was restored by increasing productivity. Those were mainly attributed to trans-province water reallocation project in the Heihe River basin.

Automatic registration of Unmanned Aerial Vehicle remote sensing images based on an improved SIFT algorithm

Unmanned Aerial Vehicle Remote Sensing (UAVRS) have developed rapidly driven mainly for military reconnaissance, earth observation and scientific data collection between military and civilian users over the past decade. However, automatic registration of UAVRS images has become a problem of blocks for the wide applications. In this paper, an algorithm based on both Random Sample Consensus (RANSAC) and least-squares method is proposed to improve the image registration performance of SIFT algorithm. On the one hand, RANSAC can remove inaccurate feature point pairs that SIFT detected. On the other hand, given all rough feature matches based on SIFT features, least-squares match is used to carry out precise matching. The experiment results show that our proposal can effectively estimate matching error with an average correct matching rate of 92.8%. And also the new algorithm had faster matching rate for the same number of images under the same experimental platform. As a result, the algorithm can improve greatly the accuracy of matching, but also to reduce the computation load based on the experiment results. Automatic registration of UAVRS images can be obtained in real time. After pre-matching by SIFT feature matching algorithm, the least squares matching is used to match accurately, which can be satisfied for the relative orientation of low-altitude remote sensing images automatically.