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Soil

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Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Scientific reports
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an...

Human health risk identification of petrochemical sites based on extreme gradient boosting.

Ecotoxicology and environmental safety
Petrochemical industry is a key industry of soil pollution, which presents great effects on human health and the ecological environment. It is of great significance to achieve rapid, economic and efficient health risk identification for petrochemical...

Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM.

Computational intelligence and neuroscience
Soil temperature ( ), a key variable in geosciences study, has generated growing interest among researchers. There are many factors affecting the spatiotemporal variation of , which poses immense challenges for the estimation. To enrich processi...

Rapid detection of ionic contents in water through sensor fusion and convolutional neural network.

Chemosphere
Salt contents in soil or groundwater are one of the primary indicators to evaluate contamination levels. Electrical conductivity (EC) or salinity information from the conventional laboratory analysis is typically inefficient in delineating contaminat...

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

PloS one
A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing ...

Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects.

Environmental monitoring and assessment
The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data a...

Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy.

Analytical methods : advancing methods and applications
The feasibility and accuracy of several combination classification models, , quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naïve Bayes (BNB), and support vector machine (SVM) classification models combined with either sequentia...

DeepSense: A Physics-Guided Deep Learning Paradigm for Anomaly Detection in Soil Gas Data at Geologic CO Storage Sites.

Environmental science & technology
Driven by the collection of enormous amounts of streaming data from sensors, and with the emergence of the internet of things, the need for developing robust detection techniques to identify data anomalies has increased recently. The algorithms for a...

Mapping high resolution National Soil Information Grids of China.

Science bulletin
Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and ...

Predicting crop root concentration factors of organic contaminants with machine learning models.

Journal of hazardous materials
Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to c...