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

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.

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

“…where WT f (j,k) is the f (n) wavelet transform coefficient, f (n) is the length of the signal sequence, ϕ , j,k (n) is the ϕ j,k (n) conjugate, and ϕ j,k (n) is the wavelet mother function. In DWT analysis, we selected the db4 mother wavelet after repeatedly testing several mother wavelet functions (dbN, sym, and coif) in the MATLAB wavelet toolbox and considering previous research results [46,60]. DWT decomposition was performed only on a binary scale in accordance with previous suggestions and preliminary experiments of hyperspectral data preprocessing.…”

Exploring Soil Fertility Variations Under Nephrolepis Biserrata In Multi-Soil Type Of Oil Palm Plantations

“…Another function of cover crops is to effectively mitigate soil erosion, preserve soil organic matter, and minimize the leaching of soil macro-and micronutrients (De Baets et al, 2011b, 2011aIkazaki et al, 2018). (Increases in pH and C organic in the soil have been reported to affect various soil nutrient adequacy parameters, including Total N, P Bray, Exc-K, Exc-Mg, Exc-Ca, Exc-Na, CEC, and BS (Shiri et al, 2020). Increases in pH and C organic contribute to the increase in Total N content in the soil (Li et al, 2022).…”

Cycling of reduced phosphorus compounds in soil and potential impacts of climate change

“…Soil P transport models typically only consider direct phosphate impacts on environmental health (Das, Huth, Probert, Condron, & Schmidt, 2019;Shiri et al, 2020;Ziadi, Whalen, Messiga, & Morel, 2013), with very little or no account of reduced P species and their influence on global P cycling. Table 1 shows common reduced P compounds from a variety of everyday sources, demonstrating how easily they can find their way into the environment for subsequent cycling.…”