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Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

Author

Listed:
  • Jalal Shiri

    (Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran)

  • Ali Keshavarzi

    (Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O.Box: 4111, Karaj 31587-77871, Iran
    Department of Mining Engineering, Hacettepe University, 06800 Beytepe, Ankara, Turkey)

  • Ozgur Kisi

    (Faculty of Natural Sciences and Engineering, Ilia State University, 0162 Tbilisi, Georgia)

  • Sahar Mohsenzadeh Karimi

    (Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran)

  • Sepideh Karimi

    (Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran)

  • Amir Hossein Nazemi

    (Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz 51666-16471, Iran)

  • Jesús Rodrigo-Comino

    (Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, 46010 Valencia, Spain
    Physical Geography, Trier University, 54286 Trier, Germany)

Abstract

Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

Suggested Citation

  • Jalal Shiri & Ali Keshavarzi & Ozgur Kisi & Sahar Mohsenzadeh Karimi & Sepideh Karimi & Amir Hossein Nazemi & Jesús Rodrigo-Comino, 2020. "Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:2150-:d:330916
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    References listed on IDEAS

    as
    1. Mak Kaboudan, 2005. "Extended Daily Exchange Rates Forecasts Using Wavelet Temporal Resolutions," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 79-107.
    2. Shiri, Jalal, 2017. "Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran," Agricultural Water Management, Elsevier, vol. 188(C), pages 101-114.
    3. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
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