Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
LI Yuping, FAN Baoyun, DONG Kangran, WAN Jinzhong, AI Yingbo, WANG Baotian. EXPERIMENTAL STUDY ON THERMAL REMEDIATION OF PETROLEUM HYDROCARBON CONTAMINATED SOILS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(4): 242-249. doi: 10.13205/j.hjgc.202404028
Citation: YU Yiqi, CHEN Nengwang, YU Qibiao, LI Shaobin, ZHANG Dongzhan, QU Fan. SELECTING TRANSFER CONDITIONS BASED ON XGBOOST TO IMPROVE WATER QUALITY PREDICTION CAPACITY OF THE LSTM MODEL[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(1): 223-234. doi: 10.13205/j.hjgc.202401029

SELECTING TRANSFER CONDITIONS BASED ON XGBOOST TO IMPROVE WATER QUALITY PREDICTION CAPACITY OF THE LSTM MODEL

doi: 10.13205/j.hjgc.202401029
  • Received Date: 2023-04-26
    Available Online: 2024-04-29
  • Accurate prediction of river water quality change is an important basis for watershed water environment management. Currently, training of the commonly used data-driven deep learning model relies on large amounts of monitoring data. However, many rivers lack monitoring data so they can't meet the accuracy requirements of water quality prediction. In this study, we have developed an approach of selecting transfer conditions based on the XGBoost model. Water quality data(temperature, pH, dissolved oxygen, total nitrogen) from automatic monitoring stations across the major river in China are used for the establishment of long and short-term memory neural network(LSTM) models. The prediction ability of the LSTM model was improved by optimizing transfer learning conditions. The results showed that: 1) the prediction accuracy of the models trained by different source domains and transfer modes was quite different; 2) when the optimal transfer conditions were selected based on the XGBoost model, the prediction error(RMSE) of the transfer model was reduced by 9.6% to 28.9%, indicating that the prediction accuracy of selected LSTM model was significantly improved. 3) selecting appropriate transfer mode, using source domain data with similar properties, and increasing the amount of training data can improve the prediction accuracy of the transfer model. The modeling approach proposed in this paper can be directly applied to the prediction of river water quality with little monitoring data, which can support watershed water environment management.
  • [1]
    FALCONI T M A,KULINKINA A V,MOHAN V R,et al.Quantifying tap-to-household water quality deterioration in urban communities in Vellore,India:the impact of spatial assumptions[J].International Journal of Hygiene and Environmental Health,2017,220(1):29-36.
    [2]
    PETER L B,NELSON T A,VAN A K L,et al.Comparison of green algal bloom intensity and related water quality parameters at paired "bloom" and "non-bloom" sites[J].Journal of Phycology,2007,43:33-34.
    [3]
    VOEROESMARTY C J,MCINTYRE P B,GESSNER M O,et al.Global threats to human water security and river biodiversity[J].Nature,2010,467(7315):555-561.
    [4]
    TANER M U,CARLETON J N,WELLMAN M.Integrated model projections of climate change impacts on a North American lake[J].Ecological Modelling,2011,222(18):3380-3393.
    [5]
    COSTA C,MARQUES L D,ALMEIDA A K,et al.Applicability of water quality models around the world-a review[J].Environmental Science and Pollution Research,2019,26(36):36141-36162.
    [6]
    陈能汪,余镒琦,陈纪新,等.人工神经网络模型在水质预警中的应用研究进展[J].环境科学学报,2021,41(12):4771-4782.
    [7]
    ZHI W,FENG D P,TSAI W P,et al.From hydrometeorology to river water quality:can a deep learning model predict dissolved oxygen at the continental scale?[J].Environmental Science & Technology,2021,55(4):2357-2368.
    [8]
    LU J,BEHBOOD V,HAO P,et al.Transfer learning using computational intelligence:a survey[J].Knowledge-Based Systems,2015,80:14-23.
    [9]
    LI X C,ZHAN D C,YANG J Q,et al.Towards understanding transfer learning algorithms using meta transfer features[C]//24th Pacific-Asia Conference on Knowledge Discovery and Data Mining,Singapore,2020.
    [10]
    RAFFEL C,SHAZEER N,ROBERTS A,et al.Exploring the limits of transfer learning with a unified text-to-text transformer[J].Journal of Machine Learning Research,2020,21:5485-5551.
    [11]
    ALAWAD M,YOON H J,GAO S,et al.Privacy-preserving deep learning nlp models for cancer registries[J].IEEE Transactions on Emerging Topics in Computing,2021,9(3):1219-1230.
    [12]
    AYANA G,DESE K,CHOE S W.Transfer learning in breast cancer diagnoses via ultrasound imaging[J].Cancers,2021,13(4):1-15.
    [13]
    HERATH S,FERNANDO B,HARANDI M.Using temporal information for recognizing actions from still images[J].Pattern Recognition,2019,96:1-11.
    [14]
    ZHOU J,CHEN Y,XIAO F,et al.Water quality prediction method based on transfer learning and echo state network[J].Journal of Circuits Systems and Computers,2021,30(14):1-12.
    [15]
    CHEN Z,XU H,JIANG P,et al.A transfer learning-based LSTM strategy for imputing large-scale consecutive missing data and its application in a water quality prediction system[J].Journal of Hydrology,2021,602:1-16.
    [16]
    PENG L,WU H,GAO M,et al.TLT:recurrent fine-tuning transfer learning for water quality long-term prediction[J].Water Research,2022,225:1-12.
    [17]
    MICHIELETTO L,OUYANG B,WILLS P.Investigation of water quality using transfer learning,phased LSTM and correntropy loss[C]//Conference on Big Data Ⅱ-Learning,Analytics,and Applications,SPIE,2020.
    [18]
    MA J,CHENG J C P,LIN C,et al.Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques[J].Atmospheric Environment,2019,214:1-9.
    [19]
    WILLARD J D,READ J S,APPLING A P,et al.Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning[J].Water Resources Research,2021,57(7):1-11.
    [20]
    MA J,LI Z,CHENG J C P,et al.Air quality prediction at new stations using spatially transferred bidirectional long short-term memory network[J].Science of the Total Environment,2020,705:1-12.
    [21]
    GUI L,XU R,LU Q,et al.Negative transfer detection in transductive transfer learning[J].International Journal of Machine Learning and Cybernetics,2018,9(2):185-197.
    [22]
    WANG S,ZHOU Y,YOU X,et al.Quantification of the antagonistic and synergistic effects of Pb2+,Cu2+,and Zn2+bioaccumulation by living Bacillus subtilis biomass using XGBoost and SHAP[J].Journal of Hazardous Materials,2023,446:1-9.
    [23]
    HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural computation,1997,9(8):1735-1780.
    [24]
    ZHOU Y L.Real-time probabilistic forecasting of river water quality under data missing situation:deep learning plus post-processing techniques[J].Journal of Hydrology,2020,589:1-10.
    [25]
    YANG Y,XIONG Q,WU C,et al.A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism[J].Environmental Science and Pollution Research,2021,28(39):55129-55139.
    [26]
    FANG X,LI X Y,ZHANG Y F,et al.Random forest-based understanding and predicting of the impacts of anthropogenic nutrient inputs on the water quality of a tropical lagoon[J].Environmental Research Letters,2021,16(5):1-12.
    [27]
    PAN S J,YANG Q A.A survey on transfer learning[J].Ieee Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
    [28]
    WEI Y,ZHANG Y,HUANG J Z,et al.Transfer learning via learning to transfer[C]//35th International Conference on Machine Learning,Sweden,2018.
    [29]
    BHAGAT S K,TUNG T M,YASEEN Z M.Heavy metal contamination prediction using ensemble model:case study of Bay sedimentation,Australia[J].Journal of Hazardous Materials,2021,403:1-13.
    [30]
    BENTEJAC C,CSORGO A,MARTINEZ-Munoz G.A comparative analysis of gradient boosting algorithms[J].Artificial Intelligence Review,2021,54(3):1937-1967.
    [31]
    DUPAS R,TAVENARD R,FOVET O,et al.Identifying seasonal patterns of phosphorus storm dynamics with dynamic time warping[J].Water Resources Research,2015,51(11):8868-8882.
    [32]
    LI L,QIAO J,YU G,et al.Interpretable tree-based ensemble model for predicting beach water quality[J].Water Research,2022,211:1-12.
    [33]
    IOVANAC N C,SAVOIE B M.Simpler is better:how linear prediction tasks improve transfer learning in chemical autoencoders[J].Journal of Physical Chemistry A,2020,124(18):3679-3685.
    [34]
    WU X T,MANTON J H,AICKELIN U,et al.Online transfer learning:negative transfer and effect of prior knowledge[C]//IEEE International Symposium on Information Theory,Australia,2021.
    [35]
    邓建军.基于Attention-LSTM与XGBoost集成机制的中国商品期货投资策略研究[D].成都:四川大学,2022.
    [36]
    黄心裕.基于数值模拟和XGBoost算法的海南清澜红树林消浪分析[D].大连:大连理工大学,2022.
  • Relative Articles

    [1]CHU Yangyang, LI Hui, ZHU Yanping, HAN Xiaomeng, SHU Shihu. A REVIEW OF RESEARCH PROGRESS OF PREDICTION MODELS FOR DISINFECTION BY-PRODUCTS: EMPIRICAL MODELS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(7): 38-48. doi: 10.13205/j.hjgc.202407004
    [2]LIU Zhi, GAO Dongming. APPLICATION AND COMPARISON OF DIFFERENT DEEP LEARNING MODELS IN RECOGNITION OF FOOD WASTE TYPES[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 254-260. doi: 10.13205/j.hjgc.202403031
    [3]LI Haihua, XIAO Baozeng, JIN Kaili, CHEN Zihan, YU Lu. CONSTUCTION AND COMPARATIVE ANALYSIS OF WATER QUALITY PREDICTION MODELS OF THE SANMENXIA RESERVOIR OF THE YELLOW RIVER[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(12): 1-7. doi: 10.13205/j.hjgc.202412001
    [4]ZHOU Jianguo, WANG Jianyu, WEI Siti. PREDICTION OF PM2.5 AND OZONE CONCENTRATION BASED ON VMD-CEEMD DECOMPOSITION AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(6): 157-165,221. doi: 10.13205/j.hjgc.202306021
    [5]LI Yuanyuan, LIU Hailong. PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(5): 163-171. doi: 10.13205/j.hjgc.202305022
    [6]WU Zi-bo, CUI Yun-xia, CAO Wei-qi, PENG Xin, ZHAO Xiu-qi-zhi-zhen. PREDICTION OF AIR POLLUTANT CONCENTRATIONS IN XUZHOU BASED ON CEEMD-BiGRU MODEL[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(9): 9-18. doi: 10.13205/j.hjgc.202209002
    [7]LI Jinjin, YANG Haidong. SOURCE TRACKING OF WASTEWATER DISCHARGE INTO RIVERS USING HYDRODYNAMIC DIFFUSION WAVE MODEL AND GENETIC ALGORITHM[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 70-76,115. doi: 10.13205/j.hjgc.202206009
    [8]FENG Jiacheng, LI Yong, LI Na, SHAN Yajie, QIAN Jianing. IMPROVEMENT OF BP MODEL BASED ON METROPOLIS CRITERION AND ITS APPLICATION IN CHLOROPHYLL-A PREDICTION FOR LAKE TAIHU[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(1): 161-168. doi: 10.13205/j.hjgc.202201024
    [9]CENG Yi-chuan, CENG Hui-guo, YUAN Wei-hao, FENG Xiang-yu, LI Bao, WANG Hua. COMPREHENSIVE ANALYSIS AND MODEL PREDICTION OF WATER QUALITY IN THE SEA-ENTRY CHANNELS OF YANGTZE ESTUARY[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(5): 95-102,108. doi: 10.13205/j.hjgc.202205014
    [10]LI Zhi-sheng, LIANG Xi-guan, JIN Yu-kai, ZHANG Hua-gang, OU Yao-chun. A COMPARATIVE STUDY ON EDICTIVE EFFECT OF PM2.5 IN BEIJING BASED ON TREE MODELS[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(6): 106-113. doi: 10.13205/j.hjgc.202106016
    [11]WU Fan, NIU Dong-jie. REVIEW ON PREDICTIVE MODELS FOR MUNICIPAL SOLID WASTE PRODUCTION[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(4): 128-133. doi: 10.13205/j.hjgc.202104020
    [12]HUANG Chun-tao, FAN Dong-ping, LU Ji-fu, LIAO Qi-feng. PREDICTION OF PM2.5 AND PM10 CONCENTRATION IN GUANGZHOU BASED ON DEEP LEARNING MODEL[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(12): 135-140. doi: 10.13205/j.hjgc.202112020
    [13]HE Zhe-xiang, LI Lei. AN AIR POLLUTANT CONCENTRATION PREDICTION MODEL BASED ON WAVELET TRANSFORM AND LSTM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(3): 111-119. doi: 10.13205/j.hjgc.202103016
    [14]LOU Dai, ZHANG Guang-zhi, REN Fu-min, XI Cheng-gang. ESTABLISHMENT OF QUANTITATIVE PREDICTION MODEL BASED ON CHARACTERISTICS OF WASTE GENERATED IN UNDERGROUND PIPELINE GALLERY CONSTRUCTION[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(3): 9-14,38. doi: 10.13205/j.hjgc.202003002
    [15]SHEN Li. PREDICTION OF DUST MOVEMENT LAW IN COAL-FIRED POWER PLANTS BASED ON GAS-PARTICLE TWO-PHASE FLOW MODEL[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 181-187,93. doi: 10.13205/j.hjgc.202006030
    [16]XUE Tong-lai, ZHAO Dong-hui, HAN Fei. SVR WATER QUALITY PREDICTION MODEL BASED ON GA OPTIMIZATION[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(3): 123-127. doi: 10.13205/j.hjgc.202003021
    [17]Zhang Feng Yin Xiuqing Dong Huizhong, . APPLICATION OF COMBINATION GREY MODEL IN CARBON EMISSIONS PREDICTION IN SHANDONG PROVINCE[J]. ENVIRONMENTAL ENGINEERING , 2015, 33(2): 147-152. doi: 10.13205/j.hjgc.201502033
  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04010203040
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 9.5 %FULLTEXT: 9.5 %META: 86.2 %META: 86.2 %PDF: 4.2 %PDF: 4.2 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 34.4 %其他: 34.4 %佛山: 0.5 %佛山: 0.5 %北京: 1.6 %北京: 1.6 %南昌: 0.5 %南昌: 0.5 %南通: 0.5 %南通: 0.5 %厦门: 4.2 %厦门: 4.2 %合肥: 2.6 %合肥: 2.6 %嘉兴: 1.1 %嘉兴: 1.1 %大同: 0.5 %大同: 0.5 %天津: 0.5 %天津: 0.5 %太原: 1.6 %太原: 1.6 %常德: 0.5 %常德: 0.5 %广州: 1.1 %广州: 1.1 %张家口: 2.1 %张家口: 2.1 %成都: 1.6 %成都: 1.6 %昆明: 2.1 %昆明: 2.1 %晋城: 0.5 %晋城: 0.5 %杭州: 2.6 %杭州: 2.6 %武汉: 1.6 %武汉: 1.6 %江门: 1.6 %江门: 1.6 %海口: 0.5 %海口: 0.5 %淮安: 0.5 %淮安: 0.5 %深圳: 0.5 %深圳: 0.5 %温州: 0.5 %温州: 0.5 %漯河: 4.2 %漯河: 4.2 %福州: 0.5 %福州: 0.5 %芒廷维尤: 21.7 %芒廷维尤: 21.7 %芝加哥: 2.6 %芝加哥: 2.6 %衡水: 0.5 %衡水: 0.5 %衢州: 0.5 %衢州: 0.5 %贵阳: 0.5 %贵阳: 0.5 %运城: 2.6 %运城: 2.6 %遵义: 0.5 %遵义: 0.5 %鄂尔多斯: 0.5 %鄂尔多斯: 0.5 %重庆: 1.1 %重庆: 1.1 %长沙: 0.5 %长沙: 0.5 %其他佛山北京南昌南通厦门合肥嘉兴大同天津太原常德广州张家口成都昆明晋城杭州武汉江门海口淮安深圳温州漯河福州芒廷维尤芝加哥衡水衢州贵阳运城遵义鄂尔多斯重庆长沙

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (163) PDF downloads(8) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return