Performance of linear mixed models and random forests for spatial prediction of soil pH

Makungwe, Mirriam; Chabala, Lydia M.; Chishala, Benson H.; Lark, R. Murray

doi:10.1016/j.geoderma.2021.115079

Cited by 37 publications

(10 citation statements)

References 48 publications

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“…In study we aimed to how key users of soil information can be incorporated when designing surveys, mapping, and quantifying uncertainty of the spatial predictions. Many DSM studies put emphasis of comparing performance of machine learning algorithms and statistical methods of spatial prediction (e.g., Vaysse and Lagacherie, 2017;Szatmári and Pásztor, 2019;Makungwe et al, 2021). It is not enough to only quantify uncertainty and leave it there.…”

Section: Way Forwardmentioning

confidence: 99%

Mapping soil micronutrient concentration at national-scale: an illustration of a decision process framework

Chagumaira

Chimungu

Nalivata

et al. 2022

Preprint

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Abstract. Mineral micronutrient deficiencies (MND), prevalent in many countries, are linked to soil type. Stakeholders in Malawi, with different information needs, require spatial information about soil micronutrients in order to design efficient interventions. These stakeholders require reliable evidence for them to act, in most cases the outcome of their decisions involves financial costs and implications for farmers' livelihoods, food security and public health. They would not want to intervene where it is unnecessary to do so or not fail to intervene where it is needed. Information about the concentration of micronutrient in soil is needed by stakeholders for decision-making. In practice this information is uncertain. Geostatistical methods and those based on algorithmically driven machine learning (ML) generate predictions of soil properties with measures of uncertainty, these measures are rarely linked to the decision-making process for which spatial information is required and it may not be clear to the stakeholders how to make use of the uncertainty information in decision-making. In this study we start from an analysis of how stakeholders, in Malawi, may use uncertain spatial information to support decisions, providing the decisions about the acceptable quality of the information and how it should be collected. We then use this analysis as a framework to compare options for spatial prediction of micronutrients in soil by ML (e.g. random forest) and geostatistical methods (e.g. linear mixed models).

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Section: Way Forwardmentioning

confidence: 99%

Mapping soil micronutrient concentration at national-scale: an illustration of a decision process framework

Chagumaira

Chimungu

Nalivata

et al. 2022

Preprint

Self Cite

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“…This is because the station contains more lime and is generally found in coastal areas. In addition, alkaline soils also contain higher levels of magnesium, calcium, potassium, and sodium ions (Makungwe et al, 2021). Stations 3 and 4 show a pattern that the lower the pH of the soil, the more acidic it is.…”

Section: Distribution Soil Ph Of To Depthmentioning

confidence: 99%

Study of Organic Carbon (OC), and Calcium Carbonate (CaCO3) in the coastal area of Aceh Besar

Azis

Irham

Sugianto

2022

Depik

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Most of the coastal areas of Aceh Besar are areas of accumulation of organic compounds, one of which is Organic Carbon (OC). The purpose of this study was to analyze the chemical content by looking vertically at the content of OC, soil pH and Calcium Carbonate (CaCO3) on the coast of Aceh Besar District. Analysis of the OC content using the Walkley and Black method, while to analyze the Calcium Carbonate (CaCO3) using the Titrimetric method. Especially for the analysis of soil pH, only use a pH meter. The results showed that each station had different levels of OC and carbonate. Generally, the presence of OC decreases with depth, while the carbonate content varies. Especially for soil pH, the pH is relative to the alkaline state for each depth. The range of OC content for all stations is 0.18 – 2.48%, Carbonate 5.36 – 13.27% and pH 6.37-8.73.Keywords:CoastalOrganic CarbonCarbonateSoil pH

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“…A 1 is the number of samples correctly classified after substitution [53]. About a third of the bootstrap training set data are not used in the model [54]. These discarded data are called out-of-bag data (OOB), which are used as verification data again.…”

Section: Random Forestmentioning

confidence: 99%

“…These discarded data are called out-of-bag data (OOB), which are used as verification data again. Therefore, out-of-bag data enhance the model's strength [54].…”

Section: Random Forestmentioning

confidence: 99%

Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

Chae

Lee

2021

Sustainability

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Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments.

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Performance of linear mixed models and random forests for spatial prediction of soil pH

Cited by 37 publications

References 48 publications

Mapping soil micronutrient concentration at national-scale: an illustration of a decision process framework

Mapping soil micronutrient concentration at national-scale: an illustration of a decision process framework

Study of Organic Carbon (OC), and Calcium Carbonate (CaCO3) in the coastal area of Aceh Besar

Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

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