[1] |
陈安, 李景吉, 王茂生, 等. 西藏“一江两河”流域生态系统服务变化及权衡与协同关系研究 [J]. 水土保持研究, 2022, 29(2): 313-319.
CHEN A, LI J J, WANG M S, et al. Research for change of ecosystem service and the tradeoff-synergy relation of the YLN Basin in the Tibet Autonomous Region [J]. Research of Soil and Water Conservation, 2022, 29(2): 313-319(in Chinese).
|
[2] |
ARDABILI S, MOSAVI A, DEHGHANI M, et al. Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review[C]//Várkonyi-Kóczy A. International Conference on Global Research and Education. Cham: Springer, 2020: 52-62.
|
[3] |
BEJOU D, WRAY B, INGRAM T N. Determinants of relationship quality: An artificial neural network analysis [J]. Journal of Business Research, 1996, 36(2): 137-143. doi: 10.1016/0148-2963(95)00100-X
|
[4] |
刁瑞翔, 青松, 越亚嫘, 等. 基于BP神经网络算法的内蒙古岱海水体透明度遥感估算 [J]. 灌溉排水学报, 2022, 41(8): 114-121.
DIAO R X, QING S, YUE Y L, et al. Using back propagation neural network algorithm and remote sensing to estimate lake water transparency [J]. Journal of Irrigation and Drainage, 2022, 41(8): 114-121(in Chinese).
|
[5] |
黄煜韬, 施维林, 纪娟, 等. 基于BP神经网络对某电镀厂土壤重金属预测及人体健康风险评价 [J]. 生态毒理学报, 2022, 17(2): 278-289.
HUANG Y T, SHI W L, JI J, et al. Prediction of soil heavy metals based on BP neural network and assessment of human health risk of an electroplating plant [J]. Asian Journal of Ecotoxicology, 2022, 17(2): 278-289(in Chinese).
|
[6] |
尤游, 张林静. 贝叶斯正则化BP神经网络在空气质量指数预测中的应用 [J]. 重庆科技学院学报(自然科学版), 2022, 24(1): 78-82.
YOU Y, ZHANG L J. Application of Bayesian regularized BP neural network in air quality index prediction [J]. Journal of Chongqing University of Science and Technology (Natural Sciences Edition), 2022, 24(1): 78-82(in Chinese).
|
[7] |
朱琳, 李明河, 陈园. 基于EHO优化的BP神经网络污水处理出水COD预测模型 [J]. 重庆工商大学学报(自然科学版), 2022, 39(3): 26-32.
ZHU L, LI M H, CHEN Y. Prediction model for effluent COD in sewage treatment based on BP neural network optimized by EHO [J]. Journal of Chongqing Technology and Business University (Natural Science Edition), 2022, 39(3): 26-32(in Chinese).
|
[8] |
ANOCHI J A, de CAMPOS VELHO H F. Climate precipitation prediction by neural network [J]. Journal of Mathematics and System Science, 2015, 5(5): 207-213.
|
[9] |
MOGHADAM S V, SHARAFATI A, FEIZI H, et al. An efficient strategy for predicting river dissolved oxygen concentration: Application of deep recurrent neural network model [J]. Environmental Monitoring and Assessment, 2021, 193(12): 798. doi: 10.1007/s10661-021-09586-x
|
[10] |
ROOKI R, ARDEJANI F D, ARYAFAR A, et al. Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine, Southeast Iran [J]. Environmental Earth Sciences, 2011, 64(5): 1303-1316. doi: 10.1007/s12665-011-0948-5
|
[11] |
符东, 吴雪菲, 易珍言, 等. 沱江水质模糊综合评价及主要污染物的预测研究 [J]. 农业环境科学学报, 2020, 39(12): 2844-2852.
FU D, WU X F, YI Z Y, et al. Fuzzy comprehensive assessment of water quality and prediction of main pollutants in the Tuo River [J]. Journal of Agro-Environment Science, 2020, 39(12): 2844-2852(in Chinese).
|
[12] |
李峻, 孙世群. 基于BP网络模型的青弋江水质预测研究 [J]. 安徽工程科技学院学报(自然科学版), 2008, 23(2): 23-26.
LI J, SUN S Q. Water quality prediction in qinyijiang River Wuhu area based on BP artificial neural network [J]. Journal of Anhui University of Technology and Science (Natural Science), 2008, 23(2): 23-26(in Chinese).
|
[13] |
周晨霓, 潘刚. 西藏拉萨河流域湿地水质分析与评价 [J]. 贵州农业科学, 2014, 42(9): 249-252.
ZHOU C N, PAN G. Analysis and evaluation of water quality of plateau wetlands in Lhasa River basin [J]. Guizhou Agricultural Sciences, 2014, 42(9): 249-252(in Chinese).
|
[14] |
李红敬, 张娜, 林小涛. 西藏雅鲁藏布江水质时空特征分析 [J]. 河南师范大学学报(自然科学版), 2010, 38(2): 126-130.
LI H J, ZHANG N, LIN X T. Spatio-temporal characteristics of Yarlung Zangbo River in Tibet [J]. Journal of Henan Normal University (Natural Science), 2010, 38(2): 126-130(in Chinese).
|
[15] |
杜梅, 张强英, 任培, 等. 西藏年楚河流域农用地土壤重金属分布与生态风险评价 [J]. 环境工程技术学报, 2022, 12(5): 1618-1625.
DU M, ZHANG Q Y, REN P, et al. Distribution of soil heavy metals and ecological risk assessment of agricultural land in Nianchu River basin, Tibet [J]. Journal of Environmental Engineering Technology, 2022, 12(5): 1618-1625(in Chinese).
|
[16] |
杨安, 邢文聪, 王小霞, 等. 西藏中部河流、湖泊表层沉积物及其周边土壤重金属来源解析及风险评价 [J]. 中国环境科学, 2020, 40(10): 4557-4567.
YANG A, XING W C, WANG X X, et al. Source and risk assessment of heavy metals in surface sediments of rivers, lakes and their surrounding soils in central Tibet [J]. China Environmental Science, 2020, 40(10): 4557-4567(in Chinese).
|
[17] |
马腾霄, 杨文光, 朱利东, 等. 雅鲁藏布江中游地貌参数特征及其构造地貌意义 [J]. 成都理工大学学报(自然科学版), 2022, 49(4): 502-512.
MA T X, YANG W G, ZHU L D, et al. Geomorphic parameters and their tectonic geomorphic significance in the middle reaches of Yarlung Zangbo River, China [J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2022, 49(4): 502-512(in Chinese).
|
[18] |
BRAY M, HAN D W. Identification of support vector machines for runoff modelling [J]. Journal of Hydroinformatics, 2004, 6(4): 265-280. doi: 10.2166/hydro.2004.0020
|
[19] |
HONG N, GUAN Y J, YANG B, et al. Quantitative source tracking of heavy metals contained in urban road deposited sediments [J]. Journal of Hazardous Materials, 2020, 393: 122362. doi: 10.1016/j.jhazmat.2020.122362
|
[20] |
万子益. 西藏主要铁矿类型地质特征简介 [J]. 矿床地质, 1986, 5(4): 24-33.
WAN Z Y. A brief introduction to the major types of iron deposits in Tibet and their geological setting [J]. Mineral Deposits, 1986, 5(4): 24-33(in Chinese).
|
[21] |
徐宗宝. 基于混合优化BP神经网络的水质预测系统的研究与实现[D]. 北京: 北京工业大学, 2020.
XU Z B. Research and realization of water quality prediction system based on hybrid optimized BP neural network[D]. Beijing: Beijing University of Technology, 2020 (in Chinese).
|
[22] |
LI P F, HUA P, GUI D W, et al. A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction [J]. Scientific Reports, 2020, 10(1): 1-15. doi: 10.1038/s41598-019-56847-4
|
[23] |
范佳妮, 王振雷, 钱锋. BP人工神经网络隐层结构设计的研究进展[J]. 控制工程, 2005, 12(增刊1): 109-113.
FAN J N, WANG Z L, QIAN F. Research progress structural design of hidden layer in BP artificial neural networks[J]. Control Engineering of China, 2005, 12(Sup 1): 109-113. (in Chinese)
|
[24] |
段宁, 杨思言, 魏婉婷. 基于BP神经网络的铅酸蓄电池厂地下水重金属浓度预测 [J]. 环境科学与技术, 2016, 39(1): 194-198.
DUAN N, YANG S Y, WEI W T. Prediction of heavy metal concentrations of the groundwater from a lead-acid battery factory based on BP neural network [J]. Environmental Science & Technology, 2016, 39(1): 194-198(in Chinese).
|
[25] |
ALEXANDER D L J, TROPSHA A, WINKLER D A. Beware of R2: Simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models [J]. Journal of Chemical Information and Modeling, 2015, 55(7): 1316-1322. doi: 10.1021/acs.jcim.5b00206
|
[26] |
ABDULLAHI N, IGWE E C, DANDAGO M A. Heavy metals contamination sources in Kano, Nigeria and their concentrations along Jakara River and its agricultural produce: A review[J]. Moroccan Journal of Agricultural Sciences 2021, 2(2): 106-113.
|
[27] |
胡志华, 高洪雷, 万汉平, 等. 西藏羊八井地热田水热蚀变的时空演化特征 [J]. 地质论评, 2022, 68(1): 359-374.
HU Z H, GAO H L, WAN H P, et al. Temporal and spatial evolution of hydrothermal alteration in the Yangbajing Geothermal Field, Xizang(Tibet) [J]. Geological Review, 2022, 68(1): 359-374(in Chinese).
|
[28] |
LIU R, XU Y, ZHANG J, et al. A comparative study of the content of heavy metals in typical metallic mine rivers of the Tibetan Plateau [J]. Geological Bulletin of China, 2018, 37(12): 2154-2168.
|
[29] |
HUANG X, SILLANPÄÄ M, DUO B, et al. Water quality in the Tibetan Plateau: Metal contents of four selected rivers [J]. Environmental Pollution, 2008, 156(2): 270-277. doi: 10.1016/j.envpol.2008.02.014
|
[30] |
HUANG X, SILLANPÄÄ M, GJESSING E T, et al. Water quality in the Tibetan Plateau: Major ions and trace elements in the headwaters of four major Asian rivers [J]. Science of the Total Environment, 2009, 407(24): 6242-6254. doi: 10.1016/j.scitotenv.2009.09.001
|
[31] |
ZHANG Y L, SILLANPÄÄ M, LI C L, et al. River water quality across the Himalayan regions: Elemental concentrations in headwaters of Yarlung Tsangbo, Indus and Ganges River [J]. Environmental Earth Sciences, 2015, 73(8): 4151-4163. doi: 10.1007/s12665-014-3702-y
|
[32] |
LIU C W. Reactive transport of arsenic-enriched geothermal spring water into a sedimentary aquifer [J]. Environmental Geochemistry and Health, 2019, 41(2): 633-648. doi: 10.1007/s10653-018-0156-2
|
[33] |
H V P T, BONNET T, GARAMBOIS S, et al. Arsenic in shallow aquifers linked to the electrical ground conductivity: The Mekong delta source example [J]. Geosciences Research, 2017, 2(3): 180-195.
|
[34] |
SHARMA S, KHANDELWAL A, AMALADASS E P, et al. Studies on DC transport and terahertz conductivity of granular molybdenum thin films for microwave radiation detector applications [J]. Journal of Applied Physics, 2020, 128(18): 183901. doi: 10.1063/5.0013939
|