Browsing by Author "Alarifi, Nassir"

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    Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-12-03) Sathiyamurthi, Subbarayan; Subbarayan, Saravanan; Ramya, Madhappan; Sivasakthi, Murugan; Gobi, Rengasamy; Qaysi, Saleh; Praveen Kumar, Sivakumar; Lee, Jinwook; Alarifi, Nassir; Wahba, Mohamed; M. Youssef, Youssef
    Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries. © 2024 by the authors.
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    Soil and Water Assessment Tool-Based Prediction of Runoff Under Scenarios of Land Use/Land Cover and Climate Change Across Indian Agro-Climatic Zones: Implications for Sustainable Development Goals
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-02-06) Subbarayan, Saravanan; Youssef, Youssef M.; Singh, Leelambar; Dąbrowska, Dominika; Alarifi, Nassir; Ramsankaran, RAAJ.; Visweshwaran, R; Saqr, Ahmed M.
    Assessing runoff under changing land use/land cover (LULC) and climatic conditions is crucial for achieving effective and sustainable water resource management on a global scale. In this study, the focus was on runoff predictions across three diverse Indian watersheds—Wunna, Bharathapuzha, and Mahanadi—spanning distinct agro-climatic zones to capture varying climatic and hydrological complexities. The soil and water assessment (SWAT) tool was used to simulate future runoff influenced by LULC and climate change and to explore the related sustainability implications, including related challenges and proposing countermeasures through a sustainable action plan (SAP). The methodology integrated high-resolution satellite imagery, the cellular automata (CA)–Markov model for projecting LULC changes, and downscaled climate data under representative concentration pathways (RCPs) 4.5 and 8.5, representing moderate and extreme climate scenarios, respectively. SWAT model calibration and validation demonstrated reliable predictive accuracy, with the coefficient of determination values (R2) > 0.50 confirming the reliability of the SWAT model in simulating hydrological processes. The results indicated significant increases in surface runoff due to urbanization, reaching >1000 mm, 600 mm, and 400 mm in southern Bharathapuzha, southeastern Wunna, and northwestern Mahanadi, respectively, especially by 2040 under RCP 8.5. These findings indicate that water quality, agricultural productivity, and urban infrastructure may be threatened. The proposed SAP includes nature-based solutions, like wetland restoration, and climate-resilient strategies to mitigate adverse effects and partially achieve sustainable development goals (SDGs) related to clean water and climate action. This research provides a robust framework for sustainable watershed management in similar regions worldwide. © 2025 by the authors.