Assessing urban soil quality by improving the method for soil environmental quality evaluation in a saline groundwater area of Iran

Sefati, Zahra; Khalilimoghadam, Bijan; Nadian, H.

doi:10.1016/j.catena.2018.10.040

Cited by 32 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tillage-layer thickness is used as an important indicator of land degradation [62]. Soil organic matter, pH, available phosphorus, and available potassium have been used to evaluate soil fertility quality [63]. Soil organic matter is the main source of crop nutrition, and is an important indicator of soil fertility [64,65].…”

Section: Superiority Testmentioning

confidence: 99%

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Wang

Yong

et al. 2021

Agriculture

View full text Add to dashboard Cite

Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

show abstract

Section: Superiority Testmentioning

confidence: 99%

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Wang

Yong

et al. 2021

Agriculture

View full text Add to dashboard Cite

show abstract

“…Finally, variables of annual population growth rate, the density of the population, the density of the road network and density of livestock were applied for the MQI calculation (Kosmas et al, 1998). Not all of these factors are available or accessible for scarce-data regions or for some regions which have different conditions of LD; however, the MEDALUS conceptual framework allows the consideration of a variety of the number of other factors when making a characterization of the quality indices (Ferrara et al, 2020;Pr av alie et al, 2020;Sefati, Khalilimoghadam, & Nadian, 2019).…”

Section: Study Areamentioning

confidence: 99%

Land degradation assessment in the dust hotspot of Southeastern Ahvaz, Iran

Poornazari¹,

Khalilimoghadam²,

Hazbavi

et al. 2020

Land Degrad Dev

Self Cite

View full text Add to dashboard Cite

The present study was conducted to assess the land degradation (LD) in the largest source area for dust storms in Southeastern Ahvaz (SEA) using a Modified Mediterranean Desertification and Land‐use (MMEDALUS) method (a modification of the MEDALUS method). The LD of the dust hotspot of SEA was evaluated using the soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and management quality index (MQI). The study indicators and final land degradation index (LDI) index were classified into four groups of low (100–120), moderate (121–135), severe (136–153), and very severe (more than 153). The results showed that the SQI, CQI, VQI, and land management quality index (MQI), respectively were 151 ± 6, 167 ± 5, 175 ± 12, and 168 ± 18 for the whole dust hotspot of SEA. The results revealed that the SQI and VQI had the minimum and maximum effect on LD assessment, respectively. The integration of study indices and indicators and their effectiveness for LD assessment showed that the study dust hotspot was rated at a mean of 165 ± 7 in the very severe degraded class based on our MMEDALUS assessment. Whereas, only about 18% of the study area scored in the severe class, and other parts are classified in the very severe class. Based on the field surveys of the dust hotspot and the results of the MMEDALUS method, suitable strategies for revitalizing the dust hotspot of SEA, were proposed using homogeneous land‐components such as the construction of windbreaks using indigenous species of the area, forestry, planting, water spreading, wetting, protection, and grazing management.

show abstract

“…Soil environmental quality, as an important component of soil quality, reflects the level of harmful substances in the soil. The scientific, accurate and comprehensive assessment of the soil environmental quality of the study area has important significance for planning land resources rationally, developing high-standard farmland, enhancing characteristic agriculture and improving the quality and efficiency of agricultural production 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Soil environmental quality in Nanling commodity grain base based on equal intercept transformation radar chart

Dong

Zhang

Yan

et al. 2021

Sci Rep

View full text Add to dashboard Cite

This paper introduces for the first time the equal intercept transformation radar chart—an improved form—to the assessment of soil environmental quality of Nanling commodity grain base. The equal intercept transformation radar chart, a visual graphical data analysis method, translates data from a numerical to graphical format. This visualization enables data presentation, analysis process and results stick out a mile and is capable of fully retaining information contained in data and excavating it in depth from geometry. Moreover, it overcomes pertinently the main defect of the conventional radar chart that the evaluation result depends heavily on the order of arrangement of indicators. The results indicated that the soil environmental quality at depths of 0–60 cm in the low mountain area of the Nanling commodity grain base was the second grade, while that in the hilly and plain areas were both first grade. The indicators of poor soil environmental quality in the low mountain area were exogenous Cd and endogenous As; those in the hilly area were exogenous Cd and endogenous As and Hg; and that in the plain area was exogenous Cd. The results were in line with the actual situation of the study area.

show abstract

Assessing urban soil quality by improving the method for soil environmental quality evaluation in a saline groundwater area of Iran

Cited by 32 publications

References 35 publications

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Land degradation assessment in the dust hotspot of Southeastern Ahvaz, Iran

Soil environmental quality in Nanling commodity grain base based on equal intercept transformation radar chart

Contact Info

Product

Resources

About