Pub Date : 2020-12-01DOI: 10.22059/JDESERT.2020.79252
Mehrdad Jeihouni, S. K. Alavipanah, A. Toomanian, A. Jafarzadeh
Soil texture is variable through space and controls most of the soil’s Physico-chemical, biological and hydrological characteristics and governs agricultural production and yield. Therefore, determining its variability and generating accurate soil texture maps have a key role in soil management and sustainable agriculture. The purpose of this study is to introduce a numerical algorithm named Least Square Support Vector Machine for Regression (LS-SVR) as a predictive model in Digital Soil Mapping (DSM) of soil texture fractions and evaluating its performances based on modeling evaluation criteria. In this study, the soil texture data of 49 soil profiles in Tabriz plain, Iran, was used. The important covariates were selected using Genetic Algorithm (GA). The model evaluation results based on ME, MAE, RMSE, and R2 indicate the high performance of LS-SVR in predicting soil texture components. The prediction RMSE for sand, silt, and clay was 6.82, 5.08 and 6.06, respectively. Silt prediction had the highest ME and the lowest MAE and RSME values. The algorithm simulated the complex spatial patterns of soil texture fractions and provided high accuracy predictions and maps. Therefore, the LS-SVR algorithm has the capability to be used as predictive models in soil texture digital mapping. This study highlighted the potential of the LS-SVR algorithm in high precision soil mapping. The generated maps can be used as basic information for environmental management and modeling.
{"title":"Soil texture fractions modeling and mapping using LS-SVR algorithm","authors":"Mehrdad Jeihouni, S. K. Alavipanah, A. Toomanian, A. Jafarzadeh","doi":"10.22059/JDESERT.2020.79252","DOIUrl":"https://doi.org/10.22059/JDESERT.2020.79252","url":null,"abstract":"Soil texture is variable through space and controls most of the soil’s Physico-chemical, biological and hydrological characteristics and governs agricultural production and yield. Therefore, determining its variability and generating accurate soil texture maps have a key role in soil management and sustainable agriculture. The purpose of this study is to introduce a numerical algorithm named Least Square Support Vector Machine for Regression (LS-SVR) as a predictive model in Digital Soil Mapping (DSM) of soil texture fractions and evaluating its performances based on modeling evaluation criteria. In this study, the soil texture data of 49 soil profiles in Tabriz plain, Iran, was used. The important covariates were selected using Genetic Algorithm (GA). The model evaluation results based on ME, MAE, RMSE, and R2 indicate the high performance of LS-SVR in predicting soil texture components. The prediction RMSE for sand, silt, and clay was 6.82, 5.08 and 6.06, respectively. Silt prediction had the highest ME and the lowest MAE and RSME values. The algorithm simulated the complex spatial patterns of soil texture fractions and provided high accuracy predictions and maps. Therefore, the LS-SVR algorithm has the capability to be used as predictive models in soil texture digital mapping. This study highlighted the potential of the LS-SVR algorithm in high precision soil mapping. The generated maps can be used as basic information for environmental management and modeling.","PeriodicalId":11118,"journal":{"name":"Desert","volume":"25 1","pages":"147-154"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47559479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.22059/JDESERT.2020.79562
Gholamreaza Barati, T. Akbariazirani, M. Moradi, A. Shamekhi
The present study was conducted to identify the synoptic patterns that could display the origin of dust-storm over southern provinces of Iran. In order to design these patterns, we selected 17 weather stations whose data-sets of visibility per meter for one decade (2000 to 2009) were provided from Meteorological Organization of Iran. This paper used daily data of Sea Level Pressure (SLP) from NCEP/NCAR for designing the synoptic patterns as composite maps for each group. The extraction of dust records from the stations and consequently the evaluation of dust-storms frequency were our primitive aims. According to results, there were totally 345 dust-storms from 2000 to 2009 in the study area. Moreover, our results revealed that the dust-storms could be classified to three groups, including pervasive, semi-pervasive, and small ones based on Dust Stations (DS) frequency. All the dust-storms comprise 2 to 41 days. This paper illustrated the patterns for all the peak dusty days of the above-mentioned groups by extracting the sea level pressure data. According to the findings, the synoptic patterns demonstrated that the Pakistan Low is an important thermal low in Southern Asia, which pumped dust from 5 routes originated from Sahel, Southern Hijaz, and Mesopotamia Plain, toward the study area, particularly during the pervasive ones. This low appeared weak and disappeared during semi-pervasive and small dust-storms.
{"title":"Identification of the synoptic patterns of dust storms over southern provinces of Iran","authors":"Gholamreaza Barati, T. Akbariazirani, M. Moradi, A. Shamekhi","doi":"10.22059/JDESERT.2020.79562","DOIUrl":"https://doi.org/10.22059/JDESERT.2020.79562","url":null,"abstract":"The present study was conducted to identify the synoptic patterns that could display the origin of dust-storm over southern provinces of Iran. In order to design these patterns, we selected 17 weather stations whose data-sets of visibility per meter for one decade (2000 to 2009) were provided from Meteorological Organization of Iran. This paper used daily data of Sea Level Pressure (SLP) from NCEP/NCAR for designing the synoptic patterns as composite maps for each group. The extraction of dust records from the stations and consequently the evaluation of dust-storms frequency were our primitive aims. According to results, there were totally 345 dust-storms from 2000 to 2009 in the study area. Moreover, our results revealed that the dust-storms could be classified to three groups, including pervasive, semi-pervasive, and small ones based on Dust Stations (DS) frequency. All the dust-storms comprise 2 to 41 days. This paper illustrated the patterns for all the peak dusty days of the above-mentioned groups by extracting the sea level pressure data. According to the findings, the synoptic patterns demonstrated that the Pakistan Low is an important thermal low in Southern Asia, which pumped dust from 5 routes originated from Sahel, Southern Hijaz, and Mesopotamia Plain, toward the study area, particularly during the pervasive ones. This low appeared weak and disappeared during semi-pervasive and small dust-storms.","PeriodicalId":11118,"journal":{"name":"Desert","volume":"25 1","pages":"249-258"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41698964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Table of Contents","authors":"","doi":"10.2307/j.ctv173f318.2","DOIUrl":"https://doi.org/10.2307/j.ctv173f318.2","url":null,"abstract":"","PeriodicalId":11118,"journal":{"name":"Desert","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47204494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DESERT AND DILIGENCE","authors":"","doi":"10.2307/j.ctv173f318.7","DOIUrl":"https://doi.org/10.2307/j.ctv173f318.7","url":null,"abstract":"","PeriodicalId":11118,"journal":{"name":"Desert","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45319587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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