Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc. Accurately modeling soil moisture has important implications in both weather and climate models. The recently available satellite-based observations give us a un…
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48 results for “soil moisture”
Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral datacs.CV
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. W…
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedule. Utilizing a state-of-the-art time-series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SM…
Hybrid models combine domain knowledge and data-driven learning for Earth observation.
problem Challenges in modelling Earth observation data with either purely mechanistic or data-driven methods.
method Gaussian process convolution models, specifically latent force models (LFMs), integrating physical knowledge into multioutput GP models.
result Model automatically estimates soil moisture persistence and discovers latent forces related to precipitation.
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Irancs.LG
Study uses machine learning to predict soil organic carbon content in northern Iran.
problem Estimating soil organic carbon content for understanding soil functions.
method Applied machine learning algorithms including DNN, SVM, ANN, etc., with genetic algorithm feature selection.
result DNN model showed highest accuracy with low prediction error and uncertainty.
DL models can outperform regionalized models in hydrology by pooling diverse data.
problem Traditional wisdom in hydrology suggests regionalization improves model performance, but DL models can unify data for better performance.
method Used DL models on pooled data from different regions, showing improved performance compared to regionalized models.
result DL models can improve performance by pooling diverse data, highlighting the 'data synergy' effect.
The performance of land surface models (LSMs) significantly affects the understanding of atmospheric and related processes. Many of the LSMs' soil and vegetation parameters were unknown so that it is crucially important to efficiently optimize them. Here I present a globally applicable and computationally efficient met…
As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to…
A hybrid machine learning and process-based-modeling (PBM) approach is proposed and evaluated at a handful of AmeriFlux sites to simulate the top-layer soil moisture state. The Hybrid-PBM (HPBM) employed here uses the Noah land-surface model integrated with Gaussian Processes. It is designed to correct the model only i…
ANN model predicts zinc leaching filter cake moisture accurately.
problem Modeling cake moisture in zinc leaching pressure filtration.
method Developed ANN model using 7 parameters.
result High accuracy in predicting cake moisture (R2 > 0.8, MSE < 1e-6).
Data mining involves the systematic analysis of large data sets, and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of a soil depends on soil fertility. Achieving and maintaining appropriate levels of soil fertility, is of utmost importance if agricultural land i…
In precision agriculture (PA), soil sampling and testing operation is prior to planting any new crop. It is an expensive operation since there are many soil characteristics to take into account. This paper gives an overview of soil characteristics and their relationships with crop yield and soil profiling. We propose a…
Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively…
Machine learning predicts plant phenotypes from soil microbiome data.
problem Predicting plant phenotypes from soil microbiome data.
method Two models (random forest and Bayesian neural network) were used to predict plant phenotypes from soil properties and microbial population density.
result Human decisions and normalization strategies significantly impact model performance.
Long-lead forecasting for spatio-temporal systems can often entail complex nonlinear dynamics that are difficult to specify it a priori. Current statistical methodologies for modeling these processes are often highly parameterized and thus, challenging to implement from a computational perspective. One potential parsim…
Custom loss functions improve accuracy of wildfire rate of spread forecasts.
problem Improving accuracy of wildfire rate of spread forecasts.
method Examined custom loss functions in machine learning models of fuel moisture content.
result Custom loss functions improved accuracy of ROS forecasts by a small amount.
Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras have a much higher degradation in performance due to soiling compared to other sensors. Thus it is critical to accurately detect soiling on th…
Machine learning improves sub-seasonal climate forecasting, especially gradient boosting and deep learning.
problem Predicting climate variables like temperature and precipitation in 2-week to 2-month time scales.
method Carefully constructed feature representations and ML approaches including gradient boosting and deep learning.
result ML methods can outperform climatological baselines and improve prediction accuracy.
Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Modelstat.AP
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is c…
Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, a…
Geotechnics adopts data-driven methods from materials informatics.
problem Soil complexity and lack of comprehensive data.
method Leveraging deep learning and transfer learning for feature extraction.
result Revolutionary potential of advanced computational tools in geotechnics.
SwiGAN generates drought scenarios for climate risk management.
problem Natural catastrophes and droughts increase insurance costs.
method Conditional GANs for generating spatio-temporal SWI maps.
result Simulates drought patterns up to 2050 for French regions.
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring change…
Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectrastat.ML
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional m…
Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of human lives lost due to this phenomenon has been a strong motivation for the research community toward the development of safe and robust techniques…
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifyin…
GeoLifeCLEF 2020 dataset pairs species observations with environmental data.
problem Understanding geographic species distribution.
method Presented a dataset of species observations with environmental features.
result Advances in location-based species recommendation.
Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can b…
Dataset for rainfall modeling in central Europe from 1981-2011.
problem Improving rainfall streamflow modeling beyond simple catchments.
method Spatially resolved meteorological and ancillary data compilation.
result Dataset for neural network-driven hydrological modeling.
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. We release the first extensive fish…
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and…
A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmla…
Study uses deep learning to predict mycotoxin levels in Irish oats.
problem Predicting mycotoxin contamination in Irish oats to improve crop quality and safety.
method Investigated neural networks and transfer learning models for multi-response prediction.
result Transfer learning model TabPFN provided the best performance.
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as…
DISTANA predicts and denoises spatial wave dynamics.
problem Identifying causality in spatially distributed, non-linear dynamical processes.
method Generative, recurrent graph convolution neural network architecture (DISTANA).
result DISTANA outperforms alternative approaches in denoising and predicting complex spatial wave propagation.
Spatial blind source separation simplifies multivariate spatial prediction.
problem Predicting multivariate measurements at unobserved locations with spatial dependencies.
method Spatial blind source separation as a pre-processing tool compared to Cokriging and neural networks.
result Spatial blind source separation simplifies spatial prediction by avoiding cross-dependencies.
Identifying homogeneous subgroups of variables can be challenging in high dimensional data analysis with highly correlated predictors. We propose a new method called Hexagonal Operator for Regression with Shrinkage and Equality Selection, HORSES for short, that simultaneously selects positively correlated variables and…
Time-warping improves RNN transfer learning for diverse time scales.
problem Transfer learning for RNNs with varying time scales.
method Time-warping rescales time in LSTM models for better transfer.
result Time-warping maintains accuracy in transferring RNNs between different time scales.
State-only imitation learning improves dexterous manipulation learning from videos.
problem High sample complexity in complex domains like dexterous manipulation.
method Train an inverse dynamics model to predict actions from states and train the policy jointly.
result Performs on par with state-action approaches and outperforms RL alone.
Survey of methods for learning from observation without requiring expert actions.
problem Lack of access to expert actions in imitation learning.
method Survey and classification of state-only imitation learning (SOIL) methods.
result Identification of open problems and future research directions.
Model infers mineral locations from geospatial data, improving predictions with auxiliary data.
problem Challenges in characterizing hidden mineral deposits underground.
method Generative modeling approach using masked and infilled geospatial maps.
result Models achieve Dice coefficients of 0.31 and recalls of 0.22 at 1×1 mi² resolution.
Deep learning predicts crop prices with improved accuracy.
problem Accurate prediction of agricultural crop prices for better decision-making.
method Innovative deep learning approach using GNNs and CNN models.
result At least 20% better performance than previous literature.
Predict water pipe failures using machine learning and survival analysis.
problem Difficulty in accessing water pipes for maintenance.
method Classical and modern classifiers for short-term prediction, survival analysis for long-term forecast, and oversampling technique for imbalanced data.
result Identifies important risk factors for water pipe failures.
An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detectioncs.CV
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the fr…
Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of …
Develops a new Gaussian process method for efficient Bayesian inference of plant root parameters in the Richards equation.
problem Estimating unknown parameters in nonlinear PDEs for agricultural studies.
method Gaussian process collocation with importance sampling and Bayesian optimization.
result Our method yields robust estimates with uncertainty quantification for plant root parameters.
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield …
New framework uses time series features for predicting streamflow in ungauged areas.
problem Predicting streamflow in areas without gauging stations.
method Developed regression-based streamflow regionalization using a wide range of time series features from large datasets.
result Certain time series features, like entropy and autocorrelation, are better predictors of streamflow than traditional catchment attributes.