Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria

Boudibi, Samir; Sakaa, Bachir; Benguega, Zineeddine; Fadlaoui, Haroun; Othman, Tarek; Bouzidi, Narimen

doi:10.1007/s11631-020-00444-0

Cited by 25 publications

(10 citation statements)

References 41 publications

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“…Mohammadpour, Shaharuddin [13] forecasted the WQI in freely constructed wetlands using a support vector machine in Malaysia. Other studies have also been undertaken in Algeria to test the effectiveness of SVM [31][32][33][34] and confirmed that SVM provides accurate results in less time-consuming and can run with fewer data than other algorithms. However, there is a lack of studies that offer decision-makers effective tools for predicting water quality index to improve water resource planning and to be used at larger scales in arid areas.…”

Section: Introductionmentioning

confidence: 84%

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Derdour¹,

Jódar-Abellán

Picazo

et al. 2022

Water

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Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.

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Section: Introductionmentioning

confidence: 84%

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Derdour¹,

Jódar-Abellán

Picazo

et al. 2022

Water

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“…The samples were segregated in the ratio 80:20 for training and later on for testing phases. MLP-NN exhibited highest accuracy with lower values of errors [6].…”

Section: Literature Reviewmentioning

confidence: 96%

Analysis of Salinity Indices Using SVM Based Approach of Ballari Town, India

Venugopal,

Kumar,

Selvaraj

et al. 2023

MMEP

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Soil salinization is a leading cause of soil and land degradation, necessitating early detection for efficient soil management. This study presents an integrated approach combining Remote Sensing and Geographic Information Systems (GIS) to identify saltaffected soils, employing the support vector machine (SVM). The research focuses on the town of Ballari in Karnataka, India, an area highly susceptible to soil salinization with severe consequences. To evaluate, monitor, and implement remedial measures, Ballari was selected as the study area. Data inputs for the SVM model were extracted from nine raster layers derived from the 2011 Landsat 9 imagery and DEM SRTM data. These layers include the Digital Elevation Model (DEM), Topographic Roughness Index (TRI), Topographic Position Index (TPI), Aspect, Slope, Normalized Differential Salinity Index (NDSI), Normalized Differential Vegetation Index (NDVI), Normalized Differential Moisture Index (NDMI), and Normalized Differential Built-up Index (NDBI). Topographical parameters, such as slope, aspect, and other metrics derived from DEM, were found to be instrumental in identifying salt-affected soil due to their ability to indicate land surface texture. Spectral indices NDSI and NDVI, computed using Red and NIR bands, along with the SWIR band, were identified as highly effective in delineating salt-affected soils. Following the layer stacking of these nine layers to form a multiband composite image, the data set was divided into a 70:30 ratio for training and testing, respectively. The model demonstrated an overall accuracy of 89.59% and a Kappa coefficient of 0.84, underlining the efficacy of this approach in predicting soil salinity.

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“…Remotely sensed data are quite often used as covariates in precision agriculture applications, since they can be correlated as proxies of key soil-forming factors [39][40][41][42]. Quite often used covariates are vegetation indices such as the normalized difference vegetation index (NDVI) [43][44][45], and recently, Synthetic Aperture Radar (SAR) was also correlated as a covariate for geostatistical modeling in water-soil sciences [46][47][48].…”

Section: Ndvi and Sar Mosaic Derivation From Sentinel-1 And -2mentioning

confidence: 99%

Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data

Silva,

Grego,

Manzione

et al. 2024

AgriEngineering

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Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and also continuous production of coffee over time. Big Data retrieved from remote sensing can be tested to improve spatial modeling. This research proposes to apply the Sentinel-2 vegetation index (NDVI) and the Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) dataset as auxiliary variables in the multivariate geostatistical modeling of coffee yield characterized by the presence of outliers and assess improvement. A total of 66 coffee yield points were sampled from a 4 ha area in a quasi-regular grid located in southeastern Brazil. Ordinary kriging (OK) and block cokriging (BCOK) were applied. Overall, coupling coffee yield with the NDVI and/or SAR in BCOK interpolation improved the accuracy of spatial interpolation of coffee yield even in the presence of outliers. Incorporating Big Data for improving the modeling for low-sampled fields requires taking into account the difference in supports between different datasets since this difference can increase uncontrolled uncertainty. In this manner, we will consider, for future research, new tests with other covariates. This research has the potential to support precision agriculture applications as site-specific plant nutrient management.

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Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria

Cited by 25 publications

References 41 publications

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Analysis of Salinity Indices Using SVM Based Approach of Ballari Town, India

Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data

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