Soil salinity mapping by different interpolation methods in Mirzaabad district, Syrdarya Province

Pulatov, Alim; Khamidov, Akhrorxon; Ахматов, Д. А.; Pulatov, Bakhtiyor; Vasenev, Viacheslav

doi:10.1088/1757-899x/883/1/012089

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…Fu et al (2021) show that kriging and IDW are comparable, meaning that there was only slightly different accuracy of both interpolation techniques. Pulatov et al (2020) prove that interpolated soil salinity values were more accurately mapped using IDW. Therefore, both spatial interpolation techniques could potentially produce accurate estimates of soil properties.…”

Section: Introductionmentioning

confidence: 63%

Temporal Changes in Spatial Patterns of Soil Properties During a Period of Rice Growth

et al. 2022

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Growing rice is complicated because of the interrelationship among production components, one of which is soil conditions. Determination of soil conditions is usually conducted by employing some soil properties. It is no doubt that soil properties have spatial and temporal characteristics. This research used two soil properties: Electrical conductivity (EC) and pH. This study aimed to use and evaluate Inverse Distance Weighted to uncover the temporal changes in pH and EC during the rice growth period. The methods used in the research were field survey, soil sampling, laboratory analysis, and data processing in Geographical Information Systems (GIS). The sample used was 30 and repeated three times under the growth period (vegetative, generative, and ripening). The results showed that there were changes in pH values of the soil in one growing season, and EC values tend to increase from the vegetative to the ripening period.

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Section: Introductionmentioning

confidence: 63%

Temporal Changes in Spatial Patterns of Soil Properties During a Period of Rice Growth

et al. 2022

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“…As results showed 238 suitability points were identified. Then, 70% of soil suitability points (166) were randomly selected for model training, and the remaining 30% (72) were used for models testing (Figure 2) [30,41].…”

Section: The Suitability Inventory Map (Sim)mentioning

confidence: 99%

Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions

et al. 2023

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Increasing agricultural production is a major concern that aims to increase income, reduce hunger, and improve other measures of well-being. Recently, the prediction of soil-suitability has become a primary topic of rising concern among academics, policymakers, and socio-economic analysts to assess dynamics of the agricultural production. This work aims to use physico-chemical and remotely sensed phenological parameters to produce soil-suitability maps (SSM) based on Machine Learning (ML) Algorithms in a semi-arid and arid region. Towards this goal an inventory of 238 suitability points has been carried out in addition to14 physico-chemical and 4 phenological parameters that have been used as inputs of machine-learning approaches which are five MLA prediction, namely RF, XgbTree, ANN, KNN and SVM. The results showed that phenological parameters were found to be the most influential in soil-suitability prediction. The validation of the Receiver Operating Characteristics (ROC) curve approach indicates an area under the curve and an AUC of more than 0.82 for all models. The best results were obtained using the XgbTree with an AUC = 0.97 in comparison to other MLA. Our findings demonstrate an excellent ability for ML models to predict the soil-suitability using physico-chemical and phenological parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.

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“…The method is applied to small and large input data sets. Various authors have applied IDW during different mappings of variables: mapping the distribution of a nickel deposit [1], geomorphology [2], estimated copper, molybdenum, gold and silver with respect to lithogeochemical data in the Kahang porphyry deposit in Central Iran [3], modeling of ionospheric time delay [4], spatial distribution maps of groundwater [5], spatial distribution of groundwater pollution maps [6], mapping of gold deposits based on drilled shallow wells [7], soil salinity mapping in the Mirzaabad District, Syrdarya Province [8], and the estimation of tin resources [9]. The estimated value of the IDW variable is calculated using the following formula: [10][11][12]:…”

Section: Introductionmentioning

confidence: 99%

Selection of the Value of Power Distance Exponent for Mapping with the Inverse Distance Weighting Method - Application in Subsurface Porosity Mapping, Northern Croatia Neogene

Barudžija,

Ivšinović,

Malvić

2024

Preprint

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The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs, discovered in the Neogene (Lower Pontian) subsurface sandstones in the northern Croatia (Pannonian Basin System). They represent small and large data samples. Also a standard statistical analysis of the data was made, followed by the qualitative-quantitative analysis of the maps, based on the selection of different values of the power distance exponent (p-value) for the K and L reservoirs maps. According to the qualitative analysis, for a small data set, p-value could be set on 1 or 2 giving the most optimal result, while for a large data set, a p value of 3 and 4 could be applied. By quantitative analysis, for the case of small data p=2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. Oppositely, a p-value of 3 and 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

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Soil salinity mapping by different interpolation methods in Mirzaabad district, Syrdarya Province

Cited by 12 publications

References 25 publications

Temporal Changes in Spatial Patterns of Soil Properties During a Period of Rice Growth

Temporal Changes in Spatial Patterns of Soil Properties During a Period of Rice Growth

Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions

Selection of the Value of Power Distance Exponent for Mapping with the Inverse Distance Weighting Method - Application in Subsurface Porosity Mapping, Northern Croatia Neogene

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