Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations. Research Paper. Published: 14 July 2021. Volume …

We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and …

Abstract. This paper reviews models of how ground-engaging tools interact with soils, the rigid-body dynamics of excavating machines, and how to combine these models to estimate soil parameters or to find faults in machines from anomalous dynamic behaviour.

Soil porosity is required to convert VSM to WFPS, with WFPS defined by the ratio of VSM to soil porosity (Equation 2). We use soil porosity from the Catchment model provided with SMAP Level 4 modeled data (Reichle et al., 2017), which provides soil porosity on a 9 km EASE grid which we regrid to the 0.25° grid for use within our study. …

To better evaluate the effects of soil layer classification on modeled diurnal LST and NSSR cycles, and more importantly, the associated SSM retrieval model of Leng et al., the soil profile has been divided into three layer zones named: upper layer (0–0.05 m), root layer (0.05–1.30 m) and bottom layer (1.30–2.50 m). The SSM is …

Review of numerical methods for modeling the interaction of soil environments with the tools of soil tillage machines M N Lysych1 1 Department Forest Industry, Metrology, Standardization and Certification, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazeva Street, Voronezh 394087, Russian Federation …

Various machine-learning models have been extensively applied to predict soil properties using infrared spectroscopy. Beyond the interpretability and transparency of these models, there is an ongoing …

Abstract. Effective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience-oriented. This …

In agricultural machinery design and optimization, the discrete element method (DEM) has played a major role due to its ability to speed up the design and manufacturing process by reducing multiple prototyping, testing, and evaluation under experimental conditions. In the field of soil dynamics, DEM has been mainly applied in …

Therefore, the DT estimated maximum allowable total As in paddy soil of 14 mg kg −1 could confidently be used as an appropriate guideline value. We further used the purposely collected field data to predict the concentration of bioavailable As in the paddy soil with the help of random forest (RF), gradient boosting machine (GBM), and LR …

Abstract In this study, a microwave-based multisatellite merged product released from the European Space Agency's Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations …

Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process‐based model. ... PurposeIntegrating process-based models with machine learning (ML) is an ...

Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast …

In this paper, machine learning algorithms are used to build a model which can classify the soil using Cone Penetration Testing (CPT) data, focusing on three regions in the US (southeastern, central, and western). Random Forest, Support Vector Machine, K Nearest Neighbors, and Extreme Gradient Boosting algorithms are the four ML …

Land cover change can affect soil organic carbon (SOC) concentrations in both top- and subsoils. Here, we propose to implement emerging machine learning (ML) …

1 Introduction. The soil-to-atmosphere CO 2 flux ("soil respiration"; R s) that results from both root and microbial respiration constitutes a large part of the terrestrial carbon cycle (Le Quéré et al., 2017).The R s flux is thought to be increasing (Hashimoto et al., 2015) due to changes in climate, land cover/disturbance, and perhaps other …

Published: 25 June 2022. Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution. Derek Pierson, Kathleen A. Lohse, William …

Clarifies the advantages and disadvantages of process-oriented (PO) models and machine learning (ML) models in space-time soil carbon modelling • Proposes a general …

In this study, machine learning techniques were used to predict the quantities of water-induced soil erosion rates, which is categorized as a regression problem. One of the crucial factors in soil erosion modeling is the validation of the models through field-based measurements ( Batista et al., 2019 ).

The loads acting on shield tunneling machines are basic parameters for the equipment design as well as key control parameters throughout the entire operation of the equipment. In the study, a mechanical analysis for the coupled interactive system between the cutterhead and the ground at the excavation face is conducted. The normal and …

Fig. 3: Projected changes in global soil carbon stock. Changes in global soil carbon stock between the current period (2005–2014) and the end of the century (2080–2099) from the original and ...

The study of soil–plant–machine interaction (SPMI) examines the system dynamics at the interface of soil, machine, and plant materials, primarily consisting of soil–machine, soil–plant, and plant–machine interactions. A thorough understanding of the mechanisms and behaviors of SPMI systems is of paramount importance to optimal …

Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health. Thus, the present study aimed to …

From data to interpretable models: machine learning for soil moisture forecasting. Regular Paper. Open access. Published: 31 August 2022. Volume 15, …

Request PDF | On Jul 15, 2020, Hassan Fathizad and others published Spatio-temporal dynamic of soil quality in the Central Iranian desert modeled with Machine Learning and Digital Soil Assessment ...

Soil temperature (ST) is an essential catchment property strongly influenced by air temperature (Ta). ST is also the key factor in sustainable agricultural developments, so researchers are still motivated to develop robust machine learning (ML) models to predict ST more reliably.

Machine Learning and Computation. Open access. ... NWM soil moisture and runoff results were extracted from Version 1.2 of the NWM retrospective run generated between 31 March 2015 and 31 December 2017 on a 1-km spatial grid. ... is modeled as a direct function of a quantity closely related to SSM (i.e., F sat and 2-m soil moisture ...

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada. * Correspondence: [email protected]. Abstract: Soil temperature is an essential factor ...

In this work, we refer to this as the soil-machine response. The same fact applies to agricultural robots or machines that are supposed to work autonomously on fields. ... In case of samples a), b) and d), the modeled RC also increases with higher working speed but less steeply as for sample e). Sample c) is more difficult to interpret, …

Descriptive summary statistic of soil carbon fractions. The descriptive statistics of the measured soil carbon fractions observed with respect to the entire dataset, calibration set (70%), and validation set (30%) are presented (Table 3). Total C ranged from 0.45 to 34.15 kg TC m −2 with a mean of 4.74 and a median of 3.32 kg TC m −2.

Hammers are most typical of impact machines. A hammer foundation-soil system consists of a frame, a falling weight, the anvil and the foundation block. This chapter discusses how the falling weight-anvil-foundation block-soil system can be modeled as two degree freedom or three degree freedom systems. Adequacy of each is mentioned depending …

A Machine Learning Data Fusion Model for Soil Moisture Retrieval. We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical ...

For this purpose, track –soil models and 3D tire model have been developed and implemented within the machine multi-body dynamics code (developed and owned by ). Moreover, compaction operations are usually needed to be modeled to understand soil and landfill compaction efficiency when these machines are used.

The agriculture industry must alter its operations in the context of climate change. Farmers can plan their irrigation operations more effectively and efficiently with the exact measurement and forecast of moisture content in their fields. Sensor-based irrigation and machine learning algorithms have the potential to facilitate farmers with significantly …

Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model. STEPHEN M. OGLE, ... Process-based model analyses are often used to estimate changes in soil organic carbon (SOC), particularly at regional to continental scales. However, uncertainties are rarely evaluated, and so it is difficult to ...

The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) …