Machine learning–informed soil conditioning for …
Abstract. Effective soil conditioning is critical for mechanized shield tunneling, yet the selection of conditioning parameters remains experience-oriented. This …
From data to interpretable models: machine learning for soil …
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 …
Scale and uncertainty in modeled soil organic carbon
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 ...
3–4D soil model as challenge for future soil research: …
We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and …
Remote Sensing | Free Full-Text | Evaluation of the Effects of Soil …
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 …
Solved Figure Q3(i) shows a soil compaction machine which
Figure Q3(i) shows a soil compaction machine which can be modeled as shown in Figure Q3(ii). The tractor of the machine has mass of m, which is connected to the roller by a flexible hitch. The hitch can be modeled as a spring with stiffness of k. The roller has mass of m and radius of r can roll without slipping on a horizontal plane as shown ...
Optimizing process-based models to predict current …
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 …
Determination of bioavailable arsenic threshold …
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 models.
Machine Learning for Modeling Soil Organic Carbon …
Land cover change can affect soil organic carbon (SOC) concentrations in both top- and subsoils. Here, we propose to implement emerging machine learning (ML) …
Estimating Spatially Explicit Irrigation Water Use Based
And they inferred irrigation from a positive bias between the remotely sensed and the modeled soil moisture at a spatial resolution of 25 km. Zhang and Long (2021) also developed a robust ...
Spatio-temporal dynamic of soil quality in the central Iranian …
@article{Fathizad2020SpatiotemporalDO, title={Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques}, author={Hassan Fathizad and Mohammad Ali Hakimzadeh Ardakani and Brandon Heung and Hamid Sodaiezadeh and Asghar Rahmani and …
Theoretical model for loads prediction on shield tunneling machine …
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 …
Machine learning assessments of soil drying for agricultural …
New machine learning models for real-time estimation of soil drying are presented. • Only remotely-sensed, public data sources are used. • Field conditions were modeled with 91–94% accuracy by two of three algorithms. • All errors fell within calculated uncertainty bounds. • The model enables decision-support services without on-site ...
An insight into machine learning models era in simulating soil…
Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques. Ecol. Indicat. (2020) M ... A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: a case study in the Yangtze Delta, China. Environ. Pollut. ...
From data to interpretable models: machine learning …
From data to interpretable models: machine learning for soil moisture forecasting. Regular Paper. Open access. Published: 31 August 2022. Volume 15, …
Digital mapping of soil properties using multiple …
Abstract. Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and …
Determination of bioavailable arsenic threshold and …
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 models.
Estimation of soil temperature from meteorological data …
Soil temperature (T s) plays a key role in physical, biological and chemical processes in terrestrial ecosystems.Accurate estimation of T s at various soil depths is crucial for land-atmosphere interactions. This study investigated the applicability of four different machine learning models, extreme learning machine (ELM), generalized …
Determination of bioavailable arsenic threshold and …
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 …
(PDF) A Review of Machine Learning Approaches to Soil
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 ...
Sensitivity of Modeled Soil NOx Emissions to Soil Moisture
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. …
Soil Modeling
The physical defection of soil resistance p j is shown in Fig. 8.3C.This represents an installed pile (that assumes no bending so that soil stresses and depth x i are uniformly distributed) and a thin slice of soil at some depth x i (Fig. 8.3 A) below the mud line.If the pile is loaded laterally with a deflection y i at depth x i, the soil stresses could yield the …
Modeling Excavator-Soil Interaction | SpringerLink
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.
Agronomy | Free Full-Text | Modeling Soil–Plant–Machine
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 …
Modeling of soil movement in the screw conveyor of the …
For this study, modeling was conducted based on the operational data of EPB machine used in a project in Seattle, WA. The earth pressure balance machine used for this project was 6.44 m in diameter, equipped with a combined screw conveyor consist of two screws in series in order to control the pressure along the screw more uniformly.The …
Incorporating soil knowledge into …
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 …
Soil erosion modeled with USLE, GIS, and remote sensing
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) …
Application of machine learning algorithms to model soil …
Soil thermal diffusivity ( k, units: m 2 /s) is defined as the ratio of thermal conductivity ( λ) to the volumetric heat capacity ( C) [ 24, 63 ], and it characterizes a soil's ability to propagate temperature changes. As illustrated in Fig. 1, methods to determine k can be sorted into three categories: direct measurement methods, analytical ...
Machine Learning Models to Predict Soil Moisture for …
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 …
Digital mapping of soil erodibility factor in northwestern
Understanding the spatial distribution of soil erodibility factor (K-factor) at the district scale is essential for managing water erosion risk. In this research, we performed to predict the low and high classes of K-factor in the northwest of Iran. Based on this, soil sampling was performed at 64 points using the grid sampling method with 1 km spacing. …
