Machine learning for digital soil mapping: Applications, challenges and suggested solutions
The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …
transforming the way soil scientists produce their maps. Within the past two decades, soil …
Conventional and digital soil mapping in Iran: Past, present, and future
Demand for accurate soil information is increasing for various applications. This paper
investigates the history of soil survey in Iran, particularly more recent developments in the …
investigates the history of soil survey in Iran, particularly more recent developments in the …
Instance weighted SMOTE by indirectly exploring the data distribution
A Zhang, H Yu, S Zhou, Z Huan, X Yang - Knowledge-Based Systems, 2022 - Elsevier
The synthetic minority oversampling technique (SMOTE) algorithm is considered a
benchmark algorithm for addressing the class imbalance learning (CIL) problem. However …
benchmark algorithm for addressing the class imbalance learning (CIL) problem. However …
Digital mapping of soil texture classes for efficient land management in the Piedmont plain of Iran
A Keshavarzi, MÁS del Árbol, F Kaya… - Soil Use and …, 2022 - Wiley Online Library
Accurate prediction of digital soil maps allows for the evaluation of larger areas with respect
to the design of efficient land management plans at the regional scale. Nowadays, there is …
to the design of efficient land management plans at the regional scale. Nowadays, there is …
Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms
Soil texture classes (STCs) influence the physical, chemical and biological properties of the
soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling …
soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling …
Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy
Previous studies have mostly focused on using visible-to-near-infrared spectral technique to
quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to …
quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to …
Synthetic resampling strategies and machine learning for digital soil mapping in Iran
R Taghizadeh‐Mehrjardi, K Schmidt… - European Journal of …, 2020 - Wiley Online Library
Most common machine learning (ML) algorithms usually work well on balanced training
sets, that is, datasets in which all classes are approximately represented equally. Otherwise …
sets, that is, datasets in which all classes are approximately represented equally. Otherwise …
Decision tree-based data mining and rule induction for identifying high quality groundwater zones to water supply management: a novel hybrid use of data mining and …
Groundwater is an important source to supply drinking water demands in both arid and semi-
arid regions. Nevertheless, locating high quality drinking water is a major challenge in such …
arid regions. Nevertheless, locating high quality drinking water is a major challenge in such …
RUESVMs: An ensemble method to handle the class imbalance problem in land cover mapping using Google Earth Engine
Timely and accurate Land Cover (LC) information is required for various applications, such
as climate change analysis and sustainable development. Although machine learning …
as climate change analysis and sustainable development. Although machine learning …
Digital mapping of soil classes using spatial extrapolation with imbalanced data
Digital mapping of soil classes using the extrapolation approach is timesaving, economically
cheap, and helps collecting soil data from areas with difficult sampling. However, it has not …
cheap, and helps collecting soil data from areas with difficult sampling. However, it has not …