AIMC Topic: Spatial Analysis

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Evaluating spatially enabled machine learning approaches to depth to bedrock mapping, Alberta, Canada.

PloS one
Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to...

Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools.

Journal of environmental management
The study investigated the spatiotemporal relationship between surface hydrological variables and groundwater quality/quantity using geostatistical and AI tools. AI models were developed to estimate groundwater quality from ground-based measurements ...

Spatiotemporal characteristics of carbon emissions in Shaanxi, China, during 2012-2019: a machine learning method with multiple variables.

Environmental science and pollution research international
Global warming attributed to the emission of greenhouse gases has caused unprecedented extreme weather events, such as excessive heatwave and rainfall, posing enormous threats to human life and sustainable development. China, as the toppest CO emitte...

Applications of Bayesian Neural Networks in Outlier Detection.

Big data
Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research t...

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.

Scientific reports
China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framew...

Hourly Water Level Forecasting in an Hydroelectric Basin Using Spatial Interpolation and Artificial Intelligence.

Sensors (Basel, Switzerland)
In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis datase...

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm.

Sensors (Basel, Switzerland)
With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor...

LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network.

Neural networks : the official journal of the International Neural Network Society
Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process data is static or dynamically co...

Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models.

Environmental science and pollution research international
Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex an...

Design and optimization of wall-climbing robot impeller by genetic algorithm based on computational fluid dynamics and kriging model.

Scientific reports
In recent years, wall-climbing robots have begun to replace manual work at heights to reduce economic losses and casualties caused by working at heights. This paper designs a negative pressure adsorption type wall-climbing robot and analyzes the inte...