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Mapping paddy rice in northeast China with a knowledge-based algorithm and time series optical, microwave, and thermal imagery

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FRONTIERS OF EARTH SCIENCE 2025年 1-16页

作者： Zhang, Chenchen Xiao, Xiangming Wang, Xinxin Qin, Yuanwei Doughty, Russell Yang, Xuebin Meng, Cheng Yao, Yuan Dong, Jinwei Univ Oklahoma Ctr Earth Observat & Modeling Sch Biol Sci Norman OK 73019 USA Fudan Univ Inst Biodivers Sci Natl Observat & Res Stn Wetland Ecosyst Yangtze Es Key Lab Biodivers Sci & Ecol EngnMinist EducSch Shanghai 200438 Peoples R China Fudan Univ Inst Ecochongming Sch Life Sci Shanghai 200438 Peoples R China Hohai Univ Coll Geog & Remote Sensing Nanjing 211100 Peoples R China Univ Oklahoma Coll Atmospher & Geog Sci Norman OK 73019 USA Syracuse Univ Geog & Environm Dept Syracuse NY 13244 USA Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Land Surface Pattern & Simulat Beijing 100101 Peoples R China

Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and Landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user's accuracy, producer's accuracy, F1-score, and overall accuracy of 0.91 +/- 0.004, 0.74 +/- 0.010, 0.82, and 0.98 +/- 0.001 (+/- value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 +/- 0.86 x 103 km2. Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.

关键词： paddy rice rice-wetland coexistence area flooding signal knowledge-based algorithm confidence map

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A knowledge-based genetic algorithm for a capacitated fuzzy p-hub centre network under uncertain information

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EXPERT SYSTEMS 2018年第4期35卷 1-1页

作者： Niknamfar, Amir Hossein Niaki, Seyed Taghi Akhavan Karimi, Marziyeh Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran Sharif Univ Technol Dept Ind Engn Tehran Iran Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

In real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type. To facilitate this difficulty, in this paper, the travel times are assumed to be characterized by trapezoidal fuzzy variables to present a fuzzy capacitated single allocation p-hub centre transportation (FCSApHCP) with uncertain information. The proposed FCSApHCP is redefined into its equivalent parametric integer non-linear programming problem using credibility constraints. The aim is to determine the location of p capacitated hubs and the allocation of centre nodes to them in order to minimize the maximum travel time in a hub-and-centre network under uncertain environments. As the FCSApHCP is NP-hard, a novel approach called knowledge-based genetic algorithm (KBGA) is developed to solve it. This algorithm utilizes 2 knowledge modules to gain good and bad knowledge about hub locations and then saves them in a good and bad hub memory, respectively. As there is no benchmark available to validate the results obtained, a genetic algorithm with multiparent crossover is designed to solve the problem as well. Then, the algorithms are tuned to solve the problem, based on which their performances are analysed and then compared together statistically. The applicability of the proposed approach and the solution methodologies are demonstrated. Finally, sensitivity analyses on the discount factor in the network and the memory sizes of the proposed KBGA are conducted to provide more insights. The results show that appropriate values of memory sizes can enhance the convergence and save population diversity of KBGA simultaneously.

关键词： capacitated p-hub centre transportation fuzzy travel time genetic algorithm knowledge-based algorithm single allocation uncertain information

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Mapping wetlands in Northeast China by using knowledge-based algorithms and microwave (PALSAR-2, Sentinel-1), optical (Sentinel-2, Landsat), and thermal (MODIS) images

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 349卷 119618页

作者： Zhang, Chenchen Xiao, Xiangming Wang, Xinxin Qin, Yuanwei Doughty, Russell Yang, Xuebin Meng, Cheng Yao, Yuan Dong, Jinwei Univ Oklahoma Ctr Earth Observat & Modeling Sch Biol Sci Norman OK 73019 USA Fudan Univ Inst Biodivers Sci Key Lab Biodivers Sci & Ecol Engn Natl Observat & Res Stn Wetland Ecosyst Yangtze Es Shanghai 200438 Peoples R China Fudan Univ Inst Ecochongming Sch Life Sci Shanghai 200438 Peoples R China Univ Oklahoma Coll Atmospher & Geog Sci Norman OK 73019 USA Syracuse Univ Geog & Environm Dept Syracuse NY 13244 USA Chinese Acad Sci Key Lab Land Surface Pattern & Simulat Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Oklahoma Sch Biol Sci 101 David L Boren Blvd Norman OK 73019 USA

Wetlands are rich in biodiversity, provide habitats for many wildlife species, and play a vital role in the transmission of bird-borne infectious diseases (e.g., highly pathogenic avian influenza). However, wetlands worldwide have been degraded or even disappeared due to natural and anthropogenic activities over the past two centuries. At present, major data products of wetlands have large uncertainties, low to moderate accuracies, and lack regular updates. Therefore, accurate and updated wetlands maps are needed for the sustainable management and conservation of wetlands. Here, we consider the remote sensing capability and define wetland types in terms of plant growth form (tree, shrub, grass), life cycle (perennial, annual), leaf seasonality (evergreen, deciduous), and canopy type (open, closed). We identify unique and stable features of individual wetland types and develop knowledge-based algorithms to map them in Northeast China at 10 m spatial resolution by using microwave (PALSAR-2, Sentinel-1), optical (Landsat (ETM+/OLI), Sentinel-2), and thermal (MODIS land surface temperature, LST) imagery in 2020. The resultant wetland map has a high overall accuracy of >95%. There were a total 154,254 km(2) of wetlands in Northeast China in 2020, which included 27,219 km(2) of seasonal open-canopy marsh, 69,158 km(2) of yearlong closed-canopy marsh, and 57,878 km(2) of deciduous forest swamp. Our results demonstrate the potential of knowledge-based algorithms and integrated multi-source image data for wetlands mapping and monitoring, which could provide improved data for the planning of wetland conservation and restoration.

关键词： Seasonal open-canopy marsh Yearlong closed-canopy marsh Forest swamp knowledge-based algorithm Google Earth engine

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A Novel Georouting Potency based Optimum Spider Monkey Approach for Avoiding Congestion in Energy Efficient Mobile Ad-hoc Network

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期127卷 1157-1186页

作者： Boddu, Nanditha Boba, Veeramallu Vatambeti, Ramesh KL Deemed Univ Comp Sci & Engn Guntur 522502 Andhra Pradesh India TKR Coll Engn & Technol Dept Comp Sci & Engn Hyderabad 500097 Telangana India CHRIST Deemed Univ Dept Comp Sci & Engn Sch Engn & Technol Kengeri Campus Bangalore 560074 Karnataka India

Mobile Ad-hoc Network (MANET) is one of the recent fields in wireless communication that involves a large number of wireless nodes, which could be changed arbitrarily with the ability to link or exit the system anytime. Nevertheless, network congestion and energy management is a major problem in MANET. Consequently, the infrastructure of a network changes frequently which results in data loss and communication overheads. Therefore, in this paper, a novel Georouting Potency based Optimum Spider Monkey algorithm has been proposed for energy management and network congestion. The proposed technique in MANET is implemented using Network Simulator2 platform and the proposed outcomes show that the node energy, overload, and delay are minimized by increasing the quantity of packets transmitted through the network. Moreover, the delay in routing overhead and congestion is decreased by the proposed protocol. Consequently, the energy management is enhanced based on constraints of delay, energy consumption, and routing overhead of the nodes. Thus the effectiveness of the proposed protocol is enhanced by selecting the optimal path within the network, decreasing the consumption of energy, and congestion avoidance. Sequentially, the performance of the proposed routing algorithm is compared to existing protocols in terms of end-to-end delay, throughput, Packet Delivery Ratio, energy consumption, etc. Thus the result shows that the lifetime of the nodes have been enhanced by a high 98% of throughput ratio, less 0.01% of energy consumption, and congestion avoidance using the proposed network.

关键词： Mobile ad hoc network Energy management knowledge-based algorithm Routing overhead Energy efficiency Spider monkey optimization

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A dynamic evolutionary multi-agent system to predict the 3D structure of proteins

A dynamic evolutionary multi-agent system to predict the 3D ...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Correa, Leonardo Arantes, Luciana Sens, Pierre Inostroza-Ponta, Mario Dorn, Marcio Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Sorbonne Univ LIP6 DELYS INRIA Paris France USACH DIINF Santiago Chile

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

The protein structure prediction is one of the key problems in Structural Bioinformatics. The protein function is directly related to its conformation and the folding can provide to researchers better understandings about the protein roles in the cell. Several computational methods have been proposed over the last decades to tackle the problem. In this paper, we propose an ab initio algorithm with database information for the protein structure prediction problem. We do so by designing some versions of a multi-agent system that use concepts of dynamic distributed evolutionary algorithms to speed up and improve the optimization by better adapting the algorithm to the target protein. The dynamic strategy consists of auto-adapting the number of optimization agents according to the needs and current status of the optimization process. The system is able to scale in/out itself depending on some diversity criteria. The algorithms also take advantage of structural knowledge from the Protein Data Bank to better guide the search and constraint the state space. To validate our computational strategies, we tested them on a set of eight protein sequences. The obtained results were topologically compatible with the experimental correspondent ones, thus corroborating the promising performance of the strategies.

关键词： optimization multi-agent system knowledge-based algorithm structural bioinformatics

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An efficient knowledge-based algorithm for the flexible job shop scheduling problem

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knowledge-based SYSTEMS 2012年 36卷 236-244页

作者： Karimi, Hamid Rahmati, Seyed Habib A. Zandieh, M. Shahid Beheshti Univ GC Dept Ind Management Management & Accounting Fac Tehran Iran Islamic Azad Univ Fac Ind & Mech Engn Qazvin Branch Dept Ind Engn Qazvin Iran

Flexible job shop scheduling problem (FJSP) is quite a difficult combinatorial model. Various metaheuristic algorithms are used to find a local or global optimum solution for this problem. Among these algorithms, variable neighborhood search (VNS) is a capable one and makes use of a systematic change of neighborhood structure for evading local optimum. The search process for finding a local or global optimum solution by VNS is totally random. This is one of the weaknesses of this algorithm. To remedy this weakness of VNS, this paper combines VNS algorithm with a knowledge module and proposes knowledge-based VNS (KBVNS). In KBVNS, the VNS part searches the solution space to find good solutions and knowledge module extracts the knowledge of good solution and feed it back to the algorithm. This would make the search process more efficient. Computational results of the paper on different size test problems prove the efficiency of our algorithm for FJS problem. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Flexible job shop scheduling problem Variable neighborhood search knowledge module Neighborhood structures knowledge-based algorithm

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Recommender System based on Extracted Data from Different Social Media. A Study of Twitter and LinkedIn 9

Recommender System based on Extracted Data from Different So...

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9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Pourheidari, Vahid Mollashahi, Ehsan Sotoodeh Vassileva, Julita Deters, Ralph Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9781538672662

The number of social media users and the amount of available digital information on them is growing exponentially. This explosive rise in the accessible data on social media may cause confusion for users and leads to unpleasant experience since the overwhelming number of various choices makes finding the items of interest too difficult. As an effective solution, recommender systems are used to predict user's responses to existing options. Each social media site attempts to develop recommending algorithms as efficiently as possible based on the users' contributions. In addition, many studies have investigated various recommending and predicting approaches for a specific application. However, considering the relationships between different data provided by people on different social media sites and using them to produce new recommender systems were the subject of only a few studies. To fill this gap, the objective of this study is developing recommender systems which are connecting two social media together. We collected data from Twitter and LinkedIn accounts of some computer scientists and developed new recommender systems: a collaborative, a content-based and two hybrids, which relate computer scientists' skills, declared on their LinkedIn profile, to their Twitter's followings. Using this data, we can generate useful recommendations not possible within just one social site. We recommend new Twitter accounts to computer scientists based on their skills and interests;and also predict their skills based on the accounts they are following on Twitter. The precision and usefulness of these recommender and predicator algorithms are investigated using a real dataset of Twitter and LinkedIn profiles;then their performances are compared to each other in terms of accuracy and time consumption.

关键词： social network analysis collaborative recommender system content-based recommender system hybrid recommender system knowledge-based algorithm

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knowledge-based algorithms in fixed-grid GA shape optimization

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2003年第4期58卷 643-660页

作者： Woon, SY Tong, LY Querin, OM Steven, GP Univ Sydney Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia Univ Leeds Sch Mech Engn Leeds LS2 9JT W Yorkshire England Univ Durham Sch Engn Durham DH1 3LE England

Shape optimization through a genetic algorithm (GA) using discrete boundary steps and the fixed-grid (FG) finite-element analysis (FEA) concept was recently introduced by the authors. In this paper, algorithms based on knowledge specific to the FG method with the GA-based shape optimization (FGGA) method are introduced that greatly increase its computational efficiency. These knowledge-based algorithms exploit the information inherent in the system at any given instance in the evolution such as string structure and fitness gradient to self-adapt the string length, population size and step magnitude. Other non-adaptive algorithms such as string grouping and deterministic local searches are also introduced to reduce the number of FEA calls. These algorithms were applied to two examples and their effects quantified. The examples show that these algorithms are highly effective in reducing the number of FEA calls required hence significantly improving the computational efficiency of the FGGA shape optimization method. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： knowledge-based algorithm genetic algorithm shape optimization

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Improving training of radial basis function network for classification of power quality disturbances

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ELECTRONICS LETTERS 2002年第17期38卷 976-977页

作者： Hoang, TA Nguyen, DT Univ Tasmania Sch Engn Hobart Tas 7001 Australia

Features extracted from non-stationary and transitory power quality disturbances using wavelet transform modulus maxima can serve as powerful discriminating features for wavelet-based classification of these disturbances. Using these features, a comprehensive 'knowledge-based' algorithm is proposed for the training of the radial basis function network classifier, so that at its convergence the network gives both the optimal feature weight vector as well as the cluster centres and scaling widths.

关键词： radial basis function network radial basis function networks pattern classification optimal feature weight vector RBF network classifier learning (artificial intelligence) cluster centres feature extraction power engineering computing convergence wavelet transforms wavelet-based classification power quality disturbances classification training wavelet transform modulus maxima scaling widths power supply quality nonstationary disturbances knowledge-based algorithm transitory power quality disturbances

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