Random forest spatial interpolation
For many decades, kriging and deterministic interpolation techniques, such as inverse
distance weighting and nearest neighbour interpolation, have been the most popular spatial …
distance weighting and nearest neighbour interpolation, have been the most popular spatial …
Prediction on the fluoride contamination in groundwater at the Datong Basin, Northern China: Comparison of random forest, logistic regression and artificial neural …
MB Nafouanti, J Li, NA Mustapha, P Uwamungu… - Applied …, 2021 - Elsevier
Groundwater fluoride is posing a health risk to humans, and analyzing groundwater quality
is time-wasting and expensive. Statistical methods provide a valuable approach to study the …
is time-wasting and expensive. Statistical methods provide a valuable approach to study the …
Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh
Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the
coupling of machine learning and remote sensing was seldom employed for soil salinity …
coupling of machine learning and remote sensing was seldom employed for soil salinity …
Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain
G Sahbeni - SN Applied Sciences, 2021 - Springer
Salt's deposition in the subsoil is known as salinization. It is caused by natural processes
such as mineral weathering or human-made activities such as irrigation with saline water …
such as mineral weathering or human-made activities such as irrigation with saline water …
Spatio-temporal prediction of air quality using distance based interpolation and deep learning techniques
The harmful impact of air pollution has drawn raising concerns from ordinary citizens,
researchers, policy makers, and smart city users. It is of great importance to identify air …
researchers, policy makers, and smart city users. It is of great importance to identify air …
Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh
The aim of the study was to estimate future groundwater potential zones based on machine
learning algorithms and climate change scenarios. Fourteen parameters (ie, curvature …
learning algorithms and climate change scenarios. Fourteen parameters (ie, curvature …
Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing
Artificial neural networks (ANNs) have been extensively used for the spatially explicit
modeling of complex geographic phenomena. However, because of the complexity of the …
modeling of complex geographic phenomena. However, because of the complexity of the …
[PDF][PDF] Evaluation of interregional freight generation modelling methods by using nationwide commodity flow survey data
W Hirun - Am. J. Eng. Applied Sci, 2016 - scholar.archive.org
A trip generation model is one of the four parts of the classical transport planning model,
which explores the volume of trip or freight at the originating and destination points of a …
which explores the volume of trip or freight at the originating and destination points of a …
Artificial neural network for flood susceptibility mapping in Bangladesh
The objective of the research is to investigate flood susceptibility in the Sylhet division of
Bangladesh. Eight influential factors (ie, elevation, slope, aspect, curvature, TWI, SPI …
Bangladesh. Eight influential factors (ie, elevation, slope, aspect, curvature, TWI, SPI …
Land cover disturbance due to tourism in Jeseniky mountain region: a remote sensing and GIS based approach
MS Boori, V Vozenilek - Earth Resources and Environmental …, 2014 - spiedigitallibrary.org
The Jeseníky Mountains tourism in Czech Republic is unique for its floristic richness, which
is caused mainly by the altitude division and polymorphism of the landscape; climate and oil …
is caused mainly by the altitude division and polymorphism of the landscape; climate and oil …