FLORE DES POPULATIONS DE FRÊNE DIMORPHE (Fraxinus dimorpha Coss. &amp; Durieu) DANS L’ATLAS SAHARIEN (MONTS DES KSOURS, ALGÉRIE OCCIDENTALE)

Hadjadj, Kouider; Guerine, Lakhdar; DERDOUR, Abdessamed

doi:10.25518/0457-4184.2440

Cited by 2 publications

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“…The north region of the Naâma is characterized by pastoral activities, particularly in hilly areas. In contrast, in the south region, agricultural fields with olive orchards, cereals, vegetables, and livestock can be found [37]. Naâma receives most of its rainfall from the north direction, with an estimated average of 287 mm [38].…”

Section: Description Of the Study Area And Data Collectionmentioning

confidence: 99%

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: Description Of the Study Area And Data Collectionmentioning

confidence: 99%

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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“…Several authors note a clear dominance of therophytes, chamephytes and hemicryptophytes in arid and semi-arid Mediterranean areas (Ozenda, 1991;Lacoste and Salanon, 2001; Aidoud, 2005; Amghar and Kadi-Hanifi, 2008;Ghafoul et al, 2019, Hadjadj et al, 2021. Therophyta is a strategy…”

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confidence: 99%

Study of Plant Diversity in the Rangelands of Stipa Tenacissima L.: Case of Sfissifa and El Biodh Region (South West Algeria)

Guerine¹,

Hadjadj²,

Mouissa³

et al. 2022

CTNS

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The floristic diversity analysis made it possible to distinguish 42 species listed in 30 genera and 17 families in the Sfissifa station and 34 species classified in 23 genera and 15 botanical families in the El Biodh station. The most representative families in the Sifissifa station are: Asteraceae (14,28 %), Amaranthaceae (11,90 %), Brassicaceae (11,90 %), Poaceae (7,14 %), Lamiaceae (7,14 %) and Cistaceae (7,14 %). The dominant families in El Biodh station are Asteraceae (17,64 %), Poaceae (11,74 %), Amaranthaceae (8,82 %), Brassicaceae (8,82 %), Lamiaceae (8,82 %) and Cistaceae (8,82 %). The other remaining families have only one or two species with estimated rates ranging from 2,38 % to 4,76% in Sfissifa and 2,94 % to 5,88 % in El Biodh. The study of biological types shows the abundance of therophytes in the two stations with 42.86 % at Sfissifa and 50 % at El Biodh, which reflects the importance of the anthropologic action and the xericity of the climate. The analysis of the biogeographical spectrum shows the dominance of native species of the Mediterranean (19 species in Sfissifa and 15 species in El Biodh). The AHC analysis of the data revealed the main factors controlling the distribution of plant formations in the two studied stations including overgrazing, salinity and desertification. The combination of prolonged droughts and anthropogenic pressures exerted on the studied areas results in the extension of species of low pastoral value and the appearance of vast expanses of bare soil reaching the stage of desertification.

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FLORE DES POPULATIONS DE FRÊNE DIMORPHE (Fraxinus dimorpha Coss. & Durieu) DANS L’ATLAS SAHARIEN (MONTS DES KSOURS, ALGÉRIE OCCIDENTALE)

Cited by 2 publications

References 0 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

Study of Plant Diversity in the Rangelands of Stipa Tenacissima L.: Case of Sfissifa and El Biodh Region (South West Algeria)

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