选择汉江中下游典型河段作为研究区域,利用2012年春、夏、秋3季水质采样结果及HJ1A卫星CCD同步多光谱数据,建立了研究区总氮浓度BP神经网络反演模型,并根据反演结果对研究区进行水质状况评价。研究结果表明基于弹性BP训练算法(启发式训练算法)的BP神经网络模型反演精度高,适用性强,可真实反映研究区总氮浓度在不同河段及不同季节中的变化情况,可较好地利用国产卫星数据开展流域水质评价工作。水质评价结果表明研究区在不同季节和不同区域水质差异较大,研究区春季总氮指标严重超标,夏、秋2季指标优于春季,下游指标优于上游。
Abstract
Typical segments in the middle and lower reaches of Hanjiang River were taken as study areas for water quality. According to sampling results and synchronized multi-spectral CCD data of HJ-1A satellite in spring, summer and autumn of 2012, we establish a retrieval model of BP neural network for TN (total nitrogen) concentration ,and assess water quality of the study areas based on the retrieval results. The results show that, on the basis of resilient BP training algorithm (heuristic-based training algorithm), the retrieval model of BP neural network established is of high accuracy and wide application fields, which can truly reflect the changes in TN concentration in different reaches and different seasons , and is easy to utilize domestic satellite data to carry out assessment work of water quality ; furthermore, assessment results indicate that water quality of the research areas varies a lot with seasons and reaches the value of TN indicator in spring significantly exceeds standard value , in other words, value of this indicator in summer or autumn is lower than that in spring. Finally, concentration of TN of downstream area is lower than that of upstream area.
关键词
汉江中下游 /
典型河段 /
水质评价 /
神经网络 /
多光谱数据 /
遥感反演模型
Key words
middle and lower reaches of Hanjiang River /
typical segments of river /
assessment of water quality /
neural network /
multi-spectral data /
remote sensing inversion model /
typical segments of river
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参考文献
[1] 邬明权, 牛 铮, 高 帅, 等. 渤海陆源入海排污口的多尺度遥感监测分析[J]. 地球信息科学学报, 2012, 14(3): 405-410.
[2] 顾 清. 浙江省饮用水水库水质演变及风险评价研究[D]. 浙江:浙江大学, 2014.
[3] 朱 琳. 西太湖宜兴段近岸水质分析与现状评价[D]. 南京:南京林业大学, 2013.
[4] BITELLI G, MANDANICI E. Atmospheric Correction Issues for Water Quality Assessment from Remote Sensing: The Case of Lake Qarun (Egypt)[C]∥ Society of Photo-optical Instrumentation Engineers. Proceedings of Remote Sensing. International Society for Optics and Photonics. Toulouse France,September 21-23, 2010: 78311Z-1-78311Z-8.
[5] MARKOGIANNI V, DIMITRIOU E, KARAOUZAS I. Water Quality Monitoring and Assessment of An Urban Mediterranean Lake Facilitated by Remote Sensing Applications[J]. Environmental Monitoring and Assessment, 2014, 186(8): 5009-5026.
[6] SYAHREZA S, MATJAFRI M Z, LIM H S, et al. Water Quality Assessment in Kelantan Delta Using Remote Sensing Technique[C]∥SPIE Security + Defence. Proceedings of International Society for Optics and Photonics. Edinburgh, United Kingdom, September 24-26, 2012: 85420X-1-85420X-7.
[7] ALPARSLAN E,AYDNER C,TUFEKCI V,et al. Water Quality Assessment at merli Dam Using Remote Sensing Techniques[J]. Environmental Monitoring and Assessment, 2007, 135(1-3): 391-398.
[8] THIEMANN S, KAUFMANN H. Determination of Chlorophyll Content and Trophic State of Lakes Using Field Spectrometer and IRS-1C Satellite Data in the Mecklenburg Lake District, Germany[J]. Remote Sensing of Environment, 2000, 73(2): 227-235.
[9] KEINER L E, YAN X H. A Neural Network Model for Estimating Sea Surface Chlorophyll and Sediments from Thematic Mapper Imagery[J]. Remote sensing of environment, 1998, 66(2): 153-165.
[10]ZHANG Y, PULLIAINEN J, KOPONEN S, et al. Application of An Empirical Neural Network to Surface Water Quality Estimation in the Gulf of Finland Using Combined Optical Data and Microwave Data[J]. Remote Sensing of Environment, 2002, 81(2): 327-336.
[11]SUN D, LI Y, WANG Q, et al. Development of Optical Criteria to Discriminate Various Types of Highly Turbid Lake Waters[J]. Hydrobiologia, 2011, 669(1): 83-104.
[12]CHAMI M, ROBILLIARD D. Inversion of Oceanic Constituents in Case I and II Waters with Genetic Programming Algorithms[J]. Applied Optics, 2002, 41(30): 6260-6275.
[13]王建平, 程声通, 贾海峰, 等. 用 TM 影像进行湖泊水色反演研究的人工神经网络模型[J]. 环境科学,2003,24(2): 73-76.
[14]吕 恒,江 南,罗潋葱. 基于TM数据的太湖叶绿素A浓度定量反演[J].地理科学,2006,26(4):472-476.
[15]赵玉芹,汪西莉,薛 赛.渭河水质遥感反演的人工神经网络模型研究[J].遥感技术与应用,2009,24(1):63-66.
[16]肖 潇,胡承芳,徐 坚.汉江水质多源遥感监测与评价方法研究技术报告[R].武汉:雷电竞体育
,2013.
[17]XIAO Xiao, HU Cheng-fang, WEN Xiong-fei, et al. A Study on Water Quality Assessment in Typical Area of Middle and Lower Reaches of the Hanjiang River[C]∥ The Center for Earth Observation and Digital Earth. Proceedings of the 35th International Symposium on Remote Sensing of Environment. Beijing, China, April 22-26, 2013: 212-1—212-8.
[18]徐祖信. 我国河流综合水质标识指数评价方法研究[J]. 同济大学学报: 自然科学版, 2005, 33(4): 482-488.
[19]WEN J, LI Z J, WEI L S, et al. The Improvements of BP Neural Network Learning Algorithm[C]∥ IEEE Signal Processing Society. Proceedings of 5th International Conference on Signal Processing. Beijing, China, August 21-25, 2000: 1647-1649.
[20]陈明忠. BP 神经网络训练算法的分析与比较[J]. 科技广场, 2010 ,(3): 24-27.
[21]吕琼帅. BP 神经网络的优化与研究[D]. 郑州: 郑州大学, 2011.
[22]张德丰,MATLAB神经网络应用设计[M].北京,机械工业出版社,2009.
[23]褚 辉,赖慧成,一种改进的BP神经网络算法及应用[J].计算机仿真,2007,24(4):75-77.
基金
国家自然科学基金项目( 41201452, 41301435);科技部国家国际合作专项项目(2012DFR70760); 国家软科学研究计划项目(2012GXS2B008) ;raybet体育在线
中央级公益性科研院所基本科研业务费(CKSF2012029/KJ,CKSF2014035/KJ,CKSF2014032/KJ)
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