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20 pages, 3754 KiB  
Article
A Spatial Multi-Criteria Decision-Making Approach to Evaluating Homogeneous Areas for Rainfed Wheat Yield Assessment
by Mohammad Reza Pooya, Ali Hasankhani, Solmaz Fathololomi and Mohammad Karimi Firozjaei
Water 2025, 17(7), 1045; https://doi.org/10.3390/w17071045 - 2 Apr 2025
Cited by 1 | Viewed by 835
Abstract
Rainfed wheat plays a vital role in global food security, particularly in regions where water availability is a limiting factor. Identifying homogeneous areas with a similar yield potential is essential for optimizing resource allocation, improving agricultural sustainability, and enhancing water resource management. Unlike [...] Read more.
Rainfed wheat plays a vital role in global food security, particularly in regions where water availability is a limiting factor. Identifying homogeneous areas with a similar yield potential is essential for optimizing resource allocation, improving agricultural sustainability, and enhancing water resource management. Unlike previous studies that primarily focused on cropland suitability, this study presents an integrated approach to delineate homogeneous areas for the rainfed wheat yield using advanced mechanistic analysis and multi-criteria decision-making techniques. Additionally, it examines the homogeneity of these areas in terms of the actual yield relative to the potential yield. Kurdistan province in Iran was selected as the study area. Key phenological stages of wheat growth—germination, flowering, and seed filling—were determined using a day-growth model. A set of four primary criteria—precipitation, temperature, soil properties, and topography—along with twenty sub-criteria were selected based on expert knowledge and previous research. The Fuzzy-AHP method was employed to assign weights to each factor, and a weighted linear combination approach was used to generate a final classification map. The results categorized the study area into five suitability classes: currently unsuitable (N2 and N1), somewhat suitable (S3), moderately suitable (S2), and very suitable (S1), in accordance with the FAO standard framework. These classifications highlighted significant yield variations among the zones. The findings revealed that the highest and lowest average rainfed wheat yields were observed in classes S1 and N2, respectively, with yield-to-potential yield ratios ranging from 75% in S1 to 20% in N2. This research underscores the potential of spatial analysis in enhancing precision agriculture and water resource management, contributing to more resilient food production systems in water-scarce regions. Full article
(This article belongs to the Section Water, Agriculture and Aquaculture)
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13 pages, 995 KiB  
Article
The Impact of Metal and Heavy Metal Concentrations on Vancomycin Resistance in Staphylococcus aureus within Milk Produced by Cattle Farms and the Health Risk Assessment in Kurdistan Province, Iran
by Yeganeh Sadeghian, Mahdieh Raeeszadeh and Hiva Karimi Darehabi
Animals 2024, 14(1), 148; https://doi.org/10.3390/ani14010148 - 2 Jan 2024
Cited by 6 | Viewed by 6850
Abstract
In today’s food landscape, the paramount focus is on ensuring food safety and hygiene. Recognizing the pivotal role of the environment and its management in safeguarding animal products, this study explores vancomycin resistance in raw milk from livestock farms in the Kurdistan province [...] Read more.
In today’s food landscape, the paramount focus is on ensuring food safety and hygiene. Recognizing the pivotal role of the environment and its management in safeguarding animal products, this study explores vancomycin resistance in raw milk from livestock farms in the Kurdistan province and its correlation with metal and heavy metal. One hundred and sixty raw milk samples were collected from various locations, with heavy metal concentrations analyzed using ICP-MS. Identification of Staphylococcus aureus and vancomycin resistance testing were conducted through culture and the Kirby–Bauer method. This study investigates the relationship between resistance and heavy metal levels, revealing that 8.75% of milk samples contained Staphylococcus aureus, with 28.58% exhibiting vancomycin resistance. Significant variations in arsenic, iron, zinc, sodium, and aluminum concentrations were observed between resistant and sensitive samples (p < 0.01). The increase in arsenic, iron, and aluminum, along with the decrease in zinc, demonstrated a significant association with vancomycin resistance (p < 0.001). Levels of lead, cadmium, mercury, zinc, and iron exceeded permissible limits (p < 0.05). The Target Hazard Quotient (THQ) for cadmium indicated a high non-carcinogenic risk, while the Target Risk (TR) for arsenic fell within the carcinogenic range. Accumulation of heavy metals has the potential to impact antibiotic resistance in milk, underscoring the imperative to control arsenic residues for national safety. Full article
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18 pages, 7275 KiB  
Article
Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms
by Himan Shahabi, Reza Ahmadi, Mohsen Alizadeh, Mazlan Hashim, Nadhir Al-Ansari, Ataollah Shirzadi, Isabelle D. Wolf and Effi Helmy Ariffin
Remote Sens. 2023, 15(12), 3112; https://doi.org/10.3390/rs15123112 - 14 Jun 2023
Cited by 26 | Viewed by 4076
Abstract
Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including [...] Read more.
Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management. Full article
(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))
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19 pages, 17180 KiB  
Article
Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran
by Rahim Tavakolifar, Himan Shahabi, Mohsen Alizadeh, Sayed M. Bateni, Mazlan Hashim, Ataollah Shirzadi, Effi Helmy Ariffin, Isabelle D. Wolf and Saman Shojae Chaeikar
Land 2023, 12(6), 1151; https://doi.org/10.3390/land12061151 - 30 May 2023
Cited by 4 | Viewed by 2192
Abstract
Landslides along the main roads in the mountains cause fatalities, ecosystem damage, and land degradation. This study mapped the susceptibility to landslides along the Saqqez-Marivan main road located in Kurdistan province, Iran, comparing an ensemble fuzzy logic with analytic network process (fuzzy logic-ANP; [...] Read more.
Landslides along the main roads in the mountains cause fatalities, ecosystem damage, and land degradation. This study mapped the susceptibility to landslides along the Saqqez-Marivan main road located in Kurdistan province, Iran, comparing an ensemble fuzzy logic with analytic network process (fuzzy logic-ANP; FLANP) and TOPSIS (fuzzy logic-TOPSIS; FLTOPSIS) in terms of their prediction capacity. First, 100 landslides identified through field surveys were randomly allocated to a 70% dataset and a 30% dataset, respectively, for training and validating the methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, land use, distance to fault, distance to a river, distance to road, soil type, curvature, and precipitation were considered. The performance of the methods was evaluated by inspecting the areas under the receiver operating curve (AUCROC). The prediction accuracies were 0.983 and 0.938, respectively, for the FLTOPSIS and FLANP methods. Our findings demonstrate that although both models are known to be promising, the FLTOPSIS method had a better capacity for predicting the susceptibility of landslides in the study area. Therefore, the susceptibility map developed through the FLTOPSIS method is suitable to inform management and planning of areas prone to landslides for land allocation and development purposes, especially in mountainous areas. Full article
(This article belongs to the Section Land–Climate Interactions)
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24 pages, 983 KiB  
Article
Climate-Smart Agriculture in Iran: Strategies, Constraints and Drivers
by Payam Memarbashi, Gholamreza Mojarradi and Marzieh Keshavarz
Sustainability 2022, 14(23), 15573; https://doi.org/10.3390/su142315573 - 23 Nov 2022
Cited by 10 | Viewed by 4887
Abstract
Although climate-smart agriculture can simultaneously decline greenhouse gas emissions, increase the adaptive capacity of farmers and improve food security under climate change, constraints and drivers of scaling up are not entirely addressed in developing countries. This qualitative case study was conducted on both [...] Read more.
Although climate-smart agriculture can simultaneously decline greenhouse gas emissions, increase the adaptive capacity of farmers and improve food security under climate change, constraints and drivers of scaling up are not entirely addressed in developing countries. This qualitative case study was conducted on both strawberry growers and agricultural experts to explore the perceived causes, evidence and impacts of climate change, adaptation strategies used by farmers, and constraints and drivers of climate-smart agriculture development on the strawberry farms in Kurdistan province, Western Iran. Findings indicated that the causes of climate change could be divided into anthropogenic and natural forces. Decreased precipitation, increased temperature, dust storms, greenhouse gases, forest fires, spring frosts, severe hail, floods and droughts comprised the most notable climate change evidence in the region. Both groups confirmed the impacts of climate change on the reduction in strawberry yield, increasing the perishability of the fruits, poverty, migration and other social problems. Adaptation strategies used by farmers are classified into technical–agricultural, water conservation, farm smartening, and institutional adaptation practices. However, poverty, the shortage of strawberry-processing industries, insufficient financial support, the presence of intermediaries and brokers, traditional cultivation, difficulties in shipping strawberry crops to the market, the lack of storage facilities and equipment and the export terminal along with the mistrust of strawberry growers in the agricultural organization hinder climate-smart agriculture development in the study area. Finally, several drivers were proposed, which were considered the basis for providing practical suggestions for planning and policy making for climate-smart agriculture development in strawberry farms. Full article
(This article belongs to the Section Air, Climate Change and Sustainability)
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13 pages, 3007 KiB  
Article
A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran
by Osama Ashraf Mohammed, Sasan Vafaei, Mehdi Mirzaei Kurdalivand, Sabri Rasooli, Chaolong Yao and Tongxin Hu
Sustainability 2022, 14(20), 13625; https://doi.org/10.3390/su142013625 - 21 Oct 2022
Cited by 10 | Viewed by 3260
Abstract
Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight [...] Read more.
Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area. Full article
(This article belongs to the Section Hazards and Sustainability)
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16 pages, 3167 KiB  
Article
Molecular Characterization of Extended Spectrum β-Lactamase (ESBL) and Virulence Gene-Factors in Uropathogenic Escherichia coli (UPEC) in Children in Duhok City, Kurdistan Region, Iraq
by Salwa Muhsin Hasan and Khalid S. Ibrahim
Antibiotics 2022, 11(9), 1246; https://doi.org/10.3390/antibiotics11091246 - 14 Sep 2022
Cited by 8 | Viewed by 3676
Abstract
Background: The presence of extended-spectrum β-lactamase (ESBL)-producing bacteria among uropathogens is significantly increasing in children all over the world. Thus, this research was conducted to investigate the prevalence of E. coli and their antimicrobial susceptibility pattern, and both genes of ESBL-producing E. coli [...] Read more.
Background: The presence of extended-spectrum β-lactamase (ESBL)-producing bacteria among uropathogens is significantly increasing in children all over the world. Thus, this research was conducted to investigate the prevalence of E. coli and their antimicrobial susceptibility pattern, and both genes of ESBL-producing E. coli resistant and virulence factor in UTIs patients among children in Duhok Province, Kurdistan, Iraq. Method: a total of 67 E. coli were identified from 260 urine samples of pediatric patients diagnosed with UTIs aged (0–15 years) which were collected from Heevi Pediatric Teaching Hospital, from August 2021 to the end of February 2022. Result: a high proportion of UPEC infections at ages <5 years and the rates among girls (88%) were significantly higher than those among the boys. A wide variety of E. coli are resistant to most antibiotics, such as Amoxicillin, Ampicillin and Tetracycline, and 64% of them were positive for ESBL. Interestingly, the presence of both the ESBL marker genes (blaTEM, and blaCTX-M) as well as both virulence marker genes (pai and hly) were detected in above 90% of E. coli. Conclusion: the data illustrate an alarming increase in UPEC with ESBL production and the emergence of multidrug-resistant drugs in the early age of children. The public health sectors should further monitor the guidelines of using antibiotics in Kurdistan, Iraq. Full article
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14 pages, 1409 KiB  
Article
Comparative Analysis of Plant Growth-Promoting Rhizobacteria (PGPR) and Chemical Fertilizers on Quantitative and Qualitative Characteristics of Rainfed Wheat
by Mohammad Hossein Sedri, Gniewko Niedbała, Ebrahim Roohi, Mohsen Niazian, Piotr Szulc, Hadi Asadi Rahmani and Vali Feiziasl
Agronomy 2022, 12(7), 1524; https://doi.org/10.3390/agronomy12071524 - 25 Jun 2022
Cited by 24 | Viewed by 5304
Abstract
The indiscriminate use of hazardous chemical fertilizers can be reduced by applying eco-friendly smart farming technologies, such as biofertilizers. The effects of five different types of plant growth-promoting rhizobacteria (PGPR), including Fla-wheat (F), Barvar-2 (B), Nitroxin (N1), Nitrokara (N2), and SWRI, and their [...] Read more.
The indiscriminate use of hazardous chemical fertilizers can be reduced by applying eco-friendly smart farming technologies, such as biofertilizers. The effects of five different types of plant growth-promoting rhizobacteria (PGPR), including Fla-wheat (F), Barvar-2 (B), Nitroxin (N1), Nitrokara (N2), and SWRI, and their integration with chemical fertilizers (50% and/or 100% need-based N, P, and Zn) on the quantitative and qualitative traits of a rainfed wheat cultivar were investigated. Field experiments, in the form of randomized complete block design (RCBD) with four replications, were conducted at the Qamloo Dryland Agricultural Research Station in Kurdistan Province, Iran, in three cropping seasons (2016–2017, 2017–2018, and 2018–2019). All the investigated characteristics of rainfed wheat were significantly affected by the integrated application of PGPR chemical fertilizers. The grain yield of treated plants with F, B, N1, and N2 PGPR plus 50% of need-based chemical fertilizers was increased by 28%, 28%, 37%, and 33%, respectively, compared with the noninoculated control. Compared with the noninoculated control, the grain protein content was increased by 0.54%, 0.88%, and 0.34% through the integrated application of F, N1, and N2 PGPR plus 50% of need-based chemical fertilizers, respectively. A combination of Nitroxin PGPR and 100% of need-based chemical fertilizers was the best treatment to increase the grain yield (56%) and grain protein content (1%) of the Azar-2 rainfed wheat cultivar. The results of this 3-year field study showed that the integrated nutrient management of PGPR-need-based N, P, and Zn chemical fertilizers can be considered a crop management tactic to increase the yield and quality of rainfed wheat and reduce chemical fertilization and subsequent environmental pollution and could be useful in terms of sustainable rainfed crop production. Full article
(This article belongs to the Special Issue Advances in PGPR (Plant Growth-Promoting Rhizobacteria))
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15 pages, 5306 KiB  
Article
Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models
by Ghafar Salavati, Ebrahim Saniei, Ebrahim Ghaderpour and Quazi K. Hassan
Sustainability 2022, 14(7), 3881; https://doi.org/10.3390/su14073881 - 25 Mar 2022
Cited by 41 | Viewed by 4178
Abstract
The risk of forest and pasture fires is one of the research topics of interest around the world. Applying precise strategies to prevent potential effects and minimize the occurrence of such incidents requires modeling. This research was conducted in the city of Sanandaj, [...] Read more.
The risk of forest and pasture fires is one of the research topics of interest around the world. Applying precise strategies to prevent potential effects and minimize the occurrence of such incidents requires modeling. This research was conducted in the city of Sanandaj, which is located in the west of the province of Kurdistan and the west of Iran. In this study, fire risk potential was assessed using weights of evidence (WoE) and statistical index (SI) models. Information about fire incidents in Sanandaj (2011–2020) was divided into two parts: educational data (2011–2017) and validation data (2018–2020). Factors considered for potential forest and rangeland fire risk in Sanandaj city included altitude, slope percentage, slope direction, distance from the road, distance from the river, land use/land cover (LULC), average annual rainfall, and average annual temperature. Finally, in order to validate the two models used, the receiver operating characteristic (ROC) curve was used. The results for the WoE and SI models showed that about 62.96% and 52.75% of the study area, respectively, were in the moderate risk to very high risk classes. In addition, the results of the ROC curve analysis showed that the WoE and SI models had area under the curve (AUC) values of 0.741 and 0.739, respectively. Although the input parameters for both models were the same, the WoE model showed a slightly higher AUC value compared to the SI model, and can potentially be used to predict future fire risk in the study area. The results of this study can help decision makers and managers take the necessary precautions to prevent forest and rangeland fires and/or to minimize fire damage. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sustainability)
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8 pages, 670 KiB  
Article
Prevalence and Molecular Characterization of Echinococcus granulosus Sensu Lato Eggs among Stray Dogs in Sulaimani Province—Kurdistan, Iraq
by Hazhar M. Aziz, Abdullah A. Hama, Mariwan A. Hama Salih and Allah Ditta
Vet. Sci. 2022, 9(4), 151; https://doi.org/10.3390/vetsci9040151 - 22 Mar 2022
Cited by 6 | Viewed by 8303
Abstract
The main goal of this study was to estimate the prevalence of Echinococcus granulosus among stray dogs, as well as its potential impact on the environmental contamination in the Kurdistan-Iraq using microscopic examination and the Copro-PCR method. The presence of taeniid eggs was [...] Read more.
The main goal of this study was to estimate the prevalence of Echinococcus granulosus among stray dogs, as well as its potential impact on the environmental contamination in the Kurdistan-Iraq using microscopic examination and the Copro-PCR method. The presence of taeniid eggs was recorded in 400 dog faeces collected from the four different regions in the Sulaimani Governorate. The parasite eggs were recovered from fresh and aged faecal samples of the dogs using two isolation techniques, a flotation method (Sheather’s solution, modified; specific gravity: d = 1.27) and a sedimentation method (formal-ether) in which the sediments from dog faeces were collected. Both methods were used for Copro-PCR to detect the presence of Echinococcus species egg through DNA using common primers designed to amplify a partial gene of cytochrome oxidase subunit 1 (COX1). The results of the microscopic examination showed a higher prevalence rate, i.e., 97 (24.25%) of E. granulosus among stray dogs generally in Sulaimani Governorate. The prevalence of E. granulosus among stray dogs according to the district area was 40, 24, 23, and 20.8% in Rzgari, Kalar, Sulaimani, and Halabja, respectively. The positive samples (n = 50) were selected for molecular confirmation, the DNA was extracted from the sediment of the positive samples and 40 (80%) samples were successfully amplified by polymerase chain reaction. The sequences show that all samples belong to the Echinococcus granulosus sensu lato (G1–G3), with slight genetic variation. It was concluded that the sediment of dog faeces can be used for DNA extraction, which is a new method that increases the sensitivity of the test, and the amount of DNA yield would be higher than the routine method, which directly uses faeces of the dogs. In addition, the molecular diagnosis was more sensitive than the microscope examination for the presence of E. granulosus eggs. The prevalence of E. granulosus in both the final hosts and the intermediate hosts must be regularly monitored. Full article
(This article belongs to the Section Veterinary Microbiology, Parasitology and Immunology)
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28 pages, 8678 KiB  
Article
A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
by Bahareh Ghasemian, Himan Shahabi, Ataollah Shirzadi, Nadhir Al-Ansari, Abolfazl Jaafari, Victoria R. Kress, Marten Geertsema, Somayeh Renoud and Anuar Ahmad
Sensors 2022, 22(4), 1573; https://doi.org/10.3390/s22041573 - 17 Feb 2022
Cited by 45 | Viewed by 4427
Abstract
We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations [...] Read more.
We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping. Full article
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12 pages, 267 KiB  
Article
Prevalence of Hepatitis B and Hepatitis C Infections among Incarcerated Individuals in Iran: A Cross-Sectional National Bio-behavioral Study in 2019
by Ghobad Moradi, Seyed Moayed Alavian, Fatemeh Gholami, Rashid Ramezani, Leila Ahangarzadeh, Yousef Moradi and Heidar Sharafi
Pathogens 2021, 10(11), 1522; https://doi.org/10.3390/pathogens10111522 - 21 Nov 2021
Cited by 4 | Viewed by 3868
Abstract
Introduction: To realize the global goals of eliminating hepatitis B virus (HBV) and hepatitis C virus (HCV) by 2030, it is necessary to monitor the status of disease among target populations and undertake the required interventions. This study is the third round of [...] Read more.
Introduction: To realize the global goals of eliminating hepatitis B virus (HBV) and hepatitis C virus (HCV) by 2030, it is necessary to monitor the status of disease among target populations and undertake the required interventions. This study is the third round of surveys to determine the prevalence of hepatitis B and C infections among incarcerated individuals in different provinces of Iran. Methods: This study was conducted in five provinces of Iran (including Kurdistan, Ardabil, West Azerbaijan, Markazi, and Semnan) in 2019. The subjects of the study were selected from incarcerated people in prisons of all provinces that had not been studied in the previous two rounds of the surveys (in 2015 and 2016) in Iran. In this study, 15 prisons were selected and 2475 incarcerated individuals were enrolled into the study based on the multistage sampling method; the selected subjects were surveyed and their dried blood spot (DBS) samples were collected to test HBsAg and HCV-Ab. In cases with a reactive result for HCV-Ab, an HCV-RNA test was also performed on their serum samples. The relationships between independent variables and outcomes were evaluated via logistic regression. Results: Of all participants (2475 subjects) enrolled in the study, 54.18% were selected from northern provinces and 45.82% from the central provinces. The prevalence of HCV-Ab and HBsAg among incarcerated individuals was 5.66% (95% CI: 4.81% to 6.64%) and 2.42% (95% CI: 1.89% to 3.11%), respectively. Among HCV-seropositive individuals, 73.68% (95% CI: 64.70% to 81.01%) had current HCV infection (detectable HCV-RNA). The results showed that histories of imprisonment, drug use, unprotected sexual contact, drug injection, tattooing, and younger age in the first-time drug use in incarcerated individuals significantly increased the risk of HCV transmission. Among these behaviors, drug injection was more likely than other behaviors to result in contracting HCV in incarcerated individuals (OR: 22.91; 95% CI: 14.92–35.18; p < 0.001). Conclusion: To achieve international and national strategies targeted to eliminate HCV and HBV by 2030, it is necessary to pay special attention to prisons in Iran. It is recommended to continue HBV vaccination of eligible people in prisons. Developing screening and treatment protocols for individuals with HCV infection in prisons can help the country to achieve HCV elimination goals. Full article
(This article belongs to the Special Issue Global Elimination of Viral Hepatitis)
20 pages, 8483 KiB  
Article
Land Use and Soil Organic Carbon Stocks—Change Detection over Time Using Digital Soil Assessment: A Case Study from Kamyaran Region, Iran (1988–2018)
by Kamal Nabiollahi, Shadi Shahlaee, Salahudin Zahedi, Ruhollah Taghizadeh-Mehrjardi, Ruth Kerry and Thomas Scholten
Agronomy 2021, 11(3), 597; https://doi.org/10.3390/agronomy11030597 - 21 Mar 2021
Cited by 11 | Viewed by 4053
Abstract
Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 [...] Read more.
Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 and 2018 were analyzed for a 177.48 km2 region in Kurdistan Province, Iran. Across the study area. 186 disturbed and undisturbed soil samples were collected at two depths (0–20 cm and 20–50 cm). Bulk density (BD), soil organic carbon (SOC), rock fragments (RockF) and SOCS were measured. Random forest was used to model the spatial variability of SOCS. Land use was mapped with supervised classification and maximum likelihood approaches. The Kappa index and overall accuracy of the supervised classification and maximum likelihood land use maps varied between 83% and 88% and 78% and 85%, respectively. The area of forest and high-quality rangeland covered 5286 ha in 1988 and decreased by almost 30% by 2018. Most of the decrease was due to the establishment of cropland and orchards, and due to overgrazing of high-quality rangeland. As expected, the results of the analysis of variance showed that mean values of SOCS for the high-quality rangeland and forest were significantly higher compared to other land use classes. Thus, transformation of land with natural vegetation like forest and high-quality rangeland led to a loss of 15,494 Mg C in the topsoil, 15,475 Mg C in the subsoil and 15,489 Mg C−1 in total. We concluded that the predominant causes of natural vegetation degradation in the study area were mostly due to the increasing need for food, anthropogenic activities such as cultivation and over grazing, lack of government landuse legislation and the results of this study are useful for land use monitoring, decision making, natural vegetation planning and other areas of research and development in Kurdistan province. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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26 pages, 4996 KiB  
Article
Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran
by Viet-Ha Nhu, Danesh Zandi, Himan Shahabi, Kamran Chapi, Ataollah Shirzadi, Nadhir Al-Ansari, Sushant K. Singh, Jie Dou and Hoang Nguyen
Appl. Sci. 2020, 10(15), 5047; https://doi.org/10.3390/app10155047 - 22 Jul 2020
Cited by 67 | Viewed by 7246
Abstract
This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We [...] Read more.
This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geospatial Big Data)
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20 pages, 4226 KiB  
Article
Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models
by Ruhollah Taghizadeh-Mehrjardi, Kamal Nabiollahi, Leila Rasoli, Ruth Kerry and Thomas Scholten
Agronomy 2020, 10(4), 573; https://doi.org/10.3390/agronomy10040573 - 17 Apr 2020
Cited by 156 | Viewed by 14123
Abstract
Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) [...] Read more.
Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested. Full article
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