Predicting landscape transitions with machine learning: a case study of the Nagavalli Basin (2000-2030) - Maarouf, Ruba; Mahammood, Vazeer; Rao, P. Jagadeeswara (Brawijaya University, 2026)
Land-use alterations and changes in land cover (LULC) in the Nagavalli Basin from the years 2000 to 2030 were incisively formulated through machine learning methods. Five years after 2000, this study was adopted to provide a thirty-year cumulative assessment period. Considering 2000, 2005, 2010, 2015, 2020, and finally 2025, satellite imagery was analyzed for LULC using a Random Forest (RF) classification model. To create the LULC scenario for the year 2030, the classified data set for this study was applied to ANN modeling techniques, projecting historical trends for future scenario forecasting. The results showed a drastic reduction of agricultural land, dwindling from 32.15% in 2000 to an estimated 6.32% by 2030. Forest cover underwent another decrease, from 50.30 to 32.56%. Impressive growth pressures on the natural ecosystem have increased aquaculture from 0.61% in 2000 to 8.03% in 2030 in terms of land use priorities. Also, a significant increase in wasteland has been projected, with estimates indicating that by 2030, wastelands will encompass 35.03% of the study area. Regarding the relative increase in built-up area percentage, a continuous upward trend indicates that gradual urbanization has also been taking place. Grasslands had an erratic pattern but skyrocketed towards the year 2030, while water bodies across the study area maintained their coherence. The combined effort by the RF and ANN models resulted in an impressive performance towards historical classifications and future predictions. The phenomenal transformation patterns of LULC indicate the anthropogenic pressure exerted on the Nagavalli Basin and would motivate and signal.