IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v32y2018i14d10.1007_s11269-018-2033-2.html
   My bibliography  Save this article

Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station

Author

Listed:
  • Rana Muhammad Adnan

    (Huazhong University of Science & Technology)

  • Xiaohui Yuan

    (Huazhong University of Science & Technology)

  • Ozgur Kisi

    (Ilia State University)

  • Muhammad Adnan

    (Chinese Academy of Science)

  • Asif Mehmood

    (Huazhong University of Science & Technology)

Abstract

Forecasting stream flow is a very importance issue in water resources planning and management. The ability of three soft computing methods, least square support vector machine (LSSVM), fuzzy genetic algorithm (FGA) and M5 model tree (M5T), in forecasting daily and monthly stream flows of poorly gauged mountainous watershed using nearby hydro-meteorological data is investigated in the current study. In the first application, monthly stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. LSSVM provides slightly better forecasts than the FGA and M5T models. Stream flow and temperature inputs generally give better forecasts compared to other inputs. In the second application, daily stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. Better results are obtained from the models comprising only stream flow inputs. In general, a better accuracy is obtained from LSSVM models in relative to the FGA and M5T. The results indicate that the monthly and daily stream flows of Hunza can be accurately forecasted by using only nearby climatic data. In the third application, daily stream flows of Hunza river are forecasted using local stream flow and climatic data and the models’ accuracy is slightly increased in relative to the previous applications. LSSVM generally performs superior to the FGA and M5T in forecasting daily stream flow of Hunza river using local stream flow and climatic inputs.

Suggested Citation

  • Rana Muhammad Adnan & Xiaohui Yuan & Ozgur Kisi & Muhammad Adnan & Asif Mehmood, 2018. "Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4469-4486, November.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:14:d:10.1007_s11269-018-2033-2
    DOI: 10.1007/s11269-018-2033-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-2033-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-018-2033-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ali Rahimikhoob, 2014. "Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 657-669, February.
    2. Krishna Singh & Mahesh Pal & V. Singh, 2010. "Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2007-2019, August.
    3. Manish Goyal, 2014. "Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1991-2003, May.
    4. Abdüsselam Altunkaynak & Mehmet Özger & Mehmet Çakmakci, 2005. "Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(5), pages 641-654, October.
    5. Juran Ahmed & Arup Sarma, 2005. "Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(2), pages 145-161, April.
    6. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    2. Ming Wei & Xue-yi You, 2022. "Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4003-4018, September.
    3. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    4. Jihong Qu & Kun Ren & Xiaoyu Shi, 2021. "Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1029-1045, February.
    5. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    6. Rana Muhammad Adnan & Andrea Petroselli & Salim Heddam & Celso Augusto Guimarães Santos & Ozgur Kisi, 2021. "Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2987-3011, February.
    7. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    8. Hui Zhang & Cunhua Pan & Yuanxin Wang & Min Xu & Fu Zhou & Xin Yang & Lou Zhu & Chao Zhao & Yangfan Song & Hongwei Chen, 2022. "Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction," Energies, MDPI, vol. 15(15), pages 1-14, July.
    9. Sungwon Kim & Meysam Alizamir & Nam Won Kim & Ozgur Kisi, 2020. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    10. Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    2. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    3. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    4. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    5. Young Hwan Choi & Donghwi Jung, 2020. "Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    6. T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
    7. Rongqi Zhang & Shanghong Zhang & Xiaoxiong Wen & Zhu Jing, 2023. "Refined Scheduling Based on Dynamic Capacity Model for Short-term Hydropower Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 21-35, January.
    8. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    9. Fang-Fang Li & Jia-Hua Wei & Xu-Dong Fu & Xin-Yu Wan, 2012. "An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4073-4090, November.
    10. Ali Suliman & Milad Jajarmizadeh & Sobri Harun & Intan Mat Darus, 2015. "Comparison of Semi-Distributed, GIS-Based Hydrological Models for the Prediction of Streamflow in a Large Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3095-3110, July.
    11. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    12. K. Ramakrishnan & C. Suribabu & T. Neelakantan, 2010. "Crop Calendar Adjustment Study for Sathanur Irrigation System in India Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3835-3851, November.
    13. Lisicki, Michal & Lubitz, William & Taylor, Graham W., 2016. "Optimal design and operation of Archimedes screw turbines using Bayesian optimization," Applied Energy, Elsevier, vol. 183(C), pages 1404-1417.
    14. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
    15. Anas Mahmood Al-Juboori, 2021. "A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 729-743, January.
    16. Onur Hınçal & A. Altan-Sakarya & A. Metin Ger, 2011. "Optimization of Multireservoir Systems by Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1465-1487, March.
    17. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 121-153, January.
    18. V. Jothiprakash & R. Arunkumar, 2013. "Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1963-1979, May.
    19. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
    20. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.

    More about this item

    Keywords

    Fuzzy logic approach; Genetic algorithm; Least square support vector machine; M5 model tree; Streamflow forecasting;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:32:y:2018:i:14:d:10.1007_s11269-018-2033-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.