IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5889-d1109802.html
   My bibliography  Save this article

Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars

Author

Listed:
  • Goksu Tuysuzoglu

    (Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

  • Kokten Ulas Birant

    (Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

  • Derya Birant

    (Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

Abstract

Predicting the rainfall status of a region has a great impact on certain factors, such as arranging agricultural activities, enabling efficient water planning, and taking precautionary measures for possible disasters (flood/drought). Due to the seriousness of the subject, the timely and accurate prediction of rainfall is highly desirable and critical for environmentally sustainable development. In this study, an ensemble of K-stars (EK-stars) approach was proposed to predict the next-day rainfall status using meteorological data, such as the temperature, humidity, pressure, and sunshine, that were collected between the years 2007 and 2017 in Australia. This study also introduced the probability-based aggregating (pagging) approach when building and combining multiple classifiers for rainfall prediction. In the implementation of the EK-stars, different experimental setups were carried out, including the change of input parameter of the algorithm, the use of different methods in the pagging step, and whether the feature selection was performed or not. The EK-stars outperformed the original K-star algorithm and the recently proposed studies in terms of the classification accuracy by making predictions that were the closest to reality. This study shows that the proposed method is promising for generating accurate predictions for the sustainable development of environmental systems.

Suggested Citation

  • Goksu Tuysuzoglu & Kokten Ulas Birant & Derya Birant, 2023. "Rainfall Prediction Using an Ensemble Machine Learning Model Based on K-Stars," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5889-:d:1109802
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5889/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/5889/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zafar Iqbal & Shamsuddin Shahid & Tarmizi Ismail & Zulfaqar Sa’adi & Aitazaz Farooque & Zaher Mundher Yaseen, 2022. "Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods," Sustainability, MDPI, vol. 14(11), pages 1-30, May.
    2. Ewa Ropelewska & Xiang Cai & Zhan Zhang & Kadir Sabanci & Muhammet Fatih Aslan, 2022. "Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels," Agriculture, MDPI, vol. 12(2), pages 1-12, February.
    3. Javed Mallick & Saeed Alqadhi & Swapan Talukdar & Majed AlSubih & Mohd. Ahmed & Roohul Abad Khan & Nabil Ben Kahla & Saud M. Abutayeh, 2021. "Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    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. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Saeed Davar & Masoud Nobahar & Mohammad Sadik Khan & Farshad Amini, 2022. "The Development of PSO-ANN and BOA-ANN Models for Predicting Matric Suction in Expansive Clay Soil," Mathematics, MDPI, vol. 10(16), pages 1-38, August.
    3. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    4. Hamna Waheed & Noureen Zafar & Waseem Akram & Awais Manzoor & Abdullah Gani & Saif ul Islam, 2022. "Deep Learning Based Disease, Pest Pattern and Nutritional Deficiency Detection System for “Zingiberaceae” Crop," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    5. Peiqiang Gao & Wenfeng Du & Qingwen Lei & Juezhi Li & Shuaiji Zhang & Ning Li, 2023. "NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1481-1497, March.
    6. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan & Necati Çetin, 2023. "Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(8), pages 1-14, April.
    7. Mahrouz Nourali, 2023. "Improved Treatment of Model Prediction Uncertainty: Estimating Rainfall using Discrete Wavelet Transform and Principal Component Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4211-4231, September.
    8. Guo-Yu Huang & Chi-Ju Lai & Ping-Feng Pai, 2022. "Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5207-5223, October.
    9. Talukdar, Swapan & Naikoo, Mohd Waseem & Mallick, Javed & Praveen, Bushra & Shahfahad, & Sharma, Pritee & Islam, Abu Reza Md. Towfiqul & Pal, Swades & Rahman, Atiqur, 2022. "Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping," Agricultural Systems, Elsevier, vol. 196(C).
    10. Ewa Ropelewska & Ahmed M. Rady & Nicholas J. Watson, 2023. "Apricot Stone Classification Using Image Analysis and Machine Learning," Sustainability, MDPI, vol. 15(12), pages 1-14, June.
    11. Hassan Faramarzi & Seyed Mohsen Hosseini & Hamid Reza Pourghasemi & Mahdi Farnaghi, 2023. "Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran," 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. 117(3), pages 3231-3255, July.

    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:gam:jsusta:v:15:y:2023:i:7:p:5889-:d:1109802. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.