Search published articles


Showing 6 results for Matinfar
Evaluating Soil- Environment Inference Model (SOLIM) for Soil Mapping Based on Fuzzy Logic in Kashan
E. Mehrabi Gohari, H. R. Matinfar, R. Taghizadeh,
Volume 21, Issue 3 (Fall 2017)
Abstract

Typical routine surveys of soils are relatively expensive in terms of time and cost and due to the fact that maps have been traditionally developed and considering their dependence on experts' opinions, updating maps is time consuming and sometimes not economical as well. While soil digital mapping, using soil various models - the Landscape, leads to simplification of the complexity found in natural soil systems and provides users with quick and inexpensive updates. In fact, the model represents a simplified form of the complex relationships between the soil and the land. This study aims to consider inferential model Soil-Land (SOLIM) in mapping and estimating soil classes in Aran area, Isfahan province. For this purpose, the SOLIM model inputs are digital geological and environmental layers of digital elevation model (DEM) including elevation, slope in percent, slop direction, curvature of the earth's surface, wetness indicator, flow direction, flow accumulation, and satellite images of Landsat 8. The seven subcategory of soil in the study area are input data of SOLIM model. Then fuzzy maps were prepared for seven types of soil and final maps of soil prediction were created by non-fuzzy action. Results showed that the SOLIM using environment variables has very high ability to separate soil types in greater detail and soils with different parent materials, geology, climate and vegetation can be separated from each other by this model with a high degree of accuracy. Comparing error matrix shows that the overall accuracy of the map derived from the model SOLIM is 92.36%.
 


Assessment of Visible and Near-Infrared Spectroscopy in Estimation of Lead and Nickel Heavy Metals content in Soil (Case study: Rey Lands)
R. Samiei Fard, H. Matinfar,
Volume 21, Issue 4 (Winter 2018)
Abstract
Reflectance spectroscopy is a fast and safe method to predict soil physicochemical and biological properties in low cost ways. Traditional methods to determine soil properties require spending a lot of time and money so that farmers are generally reluctant to use the results of laboratory measurements in soil and water management. Reflectance spectroscopy in the spectral range of 400-2500 nm (VNIR) is an alternative method for estimating the soil properties. The aim of this study was to evaluate the results of laboratory spectrometer to estimate the concentration of Lead (Pb) and Nickel (Ni) in soils irrigated with water from treatment of urban sewage sludge of Rey city and finally to compare these results with the results of measurements of atomic absorption spectrometry. In this study, the Partial Linear Square Regression (PLSR) model was used to estimate the concentration of heavy metals and Residual Mean Square Error (RMSE) was used to evaluate the performance of this model. In this research, after spectral corrections related to elimination of the water absorption bands as well as elimination of the inefficient spectrum from heavy metals estimations, the methods of estimating these elements were studied through mathematical derivation of spectral values and also the acquisition of the continuum removal spectra. The results show that the estimated values from first derivate spectra are more consistent with the results of atomic absorption spectrometers.

Evaluation of Soil Inference Model (SIE) in Soil Mapping with Emphasis on Using Expert Knowledge and Fuzzy Logic (Jolfa City-Golfaraj)
A. Fariabi, H. Matinfar,
Volume 22, Issue 3 (Fall 2018)
Abstract
One of the problems with the traditional mapping of soils is the expert’s opinion, it time-consuming and timely preparation, and the updating of the maps. While digital soil mapping, using different soil-earth models leads to the simplification of the complexity of the soil system. The purpose of this study was to investigate Soil-Environment Inference (SIE) in soil mapping with an emphasis on using the expert knowledge and fuzzy logic. For this purpose, the digital layer of geology and peripheral layers were derived from a digital elevation model including elevation, slope, and curvature of the ground surface, and auxiliary index, which comprised the input data of the SIE model. Then, the fuzzy maps prepared for the five soil types and the final map of soil prediction were created by hardening. The results showed that the SIE model, which used environmental variables, had a high ability to isolate soil types with more detailed compositions of soils with different maternal materials. The comparison of the error matrix showed that the overall accuracy of the derived map of the SIE model was equal to 75%, and the matching of the digital mapping results with conventional mapping accounted for 74.71% of the results. The difference in the compliance rate could be attributed to the difference in the nature of the two methods.

Survey Fertility Mapping of the Maghan Area Using Global Information System (GIS) and Analytic Hierarchy Process (AHP)
V. Sarvi, H. R. Matinfar,
Volume 23, Issue 1 (Spring 2019)
Abstract
In the face of rapid growth of the population and the need for food production sectors, one of the ways to achieve this is to increase the production per unit area. In modern agriculture, the preparation of soil fertility map seems to be necessary to plan for appropriate use of fertilizers for crops. This study was conducted to prepare a distinct map for evaluating the soil fertility according to soil chemical properties in 191 soil samples of Ardabil Plain in Ardabil Province. To achieve this goal, the available N and P, K, EC, Fe, Zn, Mn and the organic matter of soil were mapped using geostatistical Kriging estimator into the Geographic Information System (GIS) by the ArcGIS software. The Analytical Hierarchy Process (AHP) was used for weighting the soil fertility factors as the input data. Then, a membership functions was defined for each factor by factorial scoring and the map of soil fertility was prepared and classified by using the AHP technique into the GIS program. The results showed that most of nitrogen and phosphorus with the weight of 0.293, 0.202 had the mostly infraction on the soil fertility and production. Survey map of the distribution showed that most of the factors were studied in the northern region with the low nutrients. The results also showed that 23.7 percent of cultivated land fertility maps had a poor fertility status, 28.3 percent of the land had a moderate fertility status, 25.4 percent of the land was good and the fertile land with 22.6 percent had a very good fertility status.

Determination of the Land Suitability of Sugarcane Fields Based on Soil Quality Index Using a Geographic Information System
A. R. Zahirnia, H. R. Matinfar,
Volume 23, Issue 2 (Summer 2019)
Abstract
Determination of land suitability is one of the land evaluation methods that can determine the best use of land in each area. The purpose of this research was to determine the land suitability of Mirza Kuchak Khan's cultivation and industry fields based on the soil quality indicators and a geographic information system (GIS), and compare the results with those obtained by methods of land evaluation and root strategies. For this purpose, information on soil profiles and the amount of organic matter, phosphorus, nitrogen, potassium, zinc, drainage, texture, depth, topography, surface rocks and gravel, impervious layer depth, hydraulic conductivity, water holding capacity, electrical conductivity, reaction PH), calcium carbonate, and exchangeable sodium percent of the study area were collected. Land suitability classes based on the quality indices of fertility, chemical quality, and physical quality of soil were defined. The results showed that 27.4% of the land belonged to the very good class (S1), 62.83% of the land could be assigned to the suitable class (S2), 11.7% of the land was put in the low proportion class (S3), and 2.66% the land was in the inappropriate class (N). Also, based on the comparison of the results of the method based on the soil quality with the square root method, Kappa coefficient was 0.82, while it was equal to 0.38 for the Storie method.

Evaluation of Machine Learning Methods in Digital Mapping of Soil Organic Carbon (part of Khorramabad Plain)
H. R. Matinfar, Z. Mghsodi, S. R. Mossavi, M. Jalali,
Volume 24, Issue 4 (Winter 2021)
Abstract
Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb