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Feng L, Khalil U, Aslam B, Ghaffar B, Tariq A, Jamil A, Farhan M, Aslam M, Soufan W. Evaluation of soil texture classification from orthodox interpolation and machine learning techniques. ENVIRONMENTAL RESEARCH 2024; 246:118075. [PMID: 38159666 DOI: 10.1016/j.envres.2023.118075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024]
Abstract
The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.
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Affiliation(s)
- Lei Feng
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; College of Environment and Ecology, Chongqing University, Chongqing, China
| | - Umer Khalil
- ITC Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the Netherlands
| | - Bilal Aslam
- Department of Earth Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Bushra Ghaffar
- Department of Environmental Science, Faculty of Sciences, International Islamic University, Islamabad, Pakistan
| | - Aqil Tariq
- Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS, 39762-9690, USA.
| | - Ahsan Jamil
- Department of Plant and Environmental Sciences, New Mexico State University, 3170S Espina Str., Las Cruces, NM, 88003, USA
| | - Muhammad Farhan
- School of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, China
| | - Muhammad Aslam
- Department of Computer Science, Aberystwyth University, UK
| | - Walid Soufan
- Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia
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Naqvi SAA, Sajjad M, Tariq A, Sajjad M, Waseem LA, Karuppannan S, Rehman A, Hassan M, Al-Ahmadi S, Hatamleh WA. Societal knowledge, attitude, and practices towards dengue and associated factors in epidemic-hit areas: Geoinformation assisted empirical evidence. Heliyon 2024; 10:e23151. [PMID: 38223736 PMCID: PMC10784149 DOI: 10.1016/j.heliyon.2023.e23151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/25/2023] [Accepted: 11/28/2023] [Indexed: 01/16/2024] Open
Abstract
Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.
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Affiliation(s)
- Syed Ali Asad Naqvi
- Department of Geography, Government College University Faisalabad, Faisalabad, 38000, Punjab, Pakistan
| | - Muhammad Sajjad
- Department of Geography, Government College University Faisalabad, Faisalabad, 38000, Punjab, Pakistan
| | - Aqil Tariq
- Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, 39762-9690, MS, USA
| | - Muhammad Sajjad
- Centre for Geo-computation Studies and Department of Geography, Hong Kong Baptist University, Hong Kong Special Administrative Region, China
| | - Liaqat Ali Waseem
- Department of Geography, Government College University Faisalabad, Faisalabad, 38000, Punjab, Pakistan
| | - Shankar Karuppannan
- Department of Applied Geology, School of Applied Natural Sciences, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia
| | - Adnanul Rehman
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China
| | - Mujtaba Hassan
- Department of Space Science, Institute of Space Technology, Main Islamabad Expressway, Islamabad, Pakistan
| | - Saad Al-Ahmadi
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia
| | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia
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