Search Results (3)

Land use and land cover change is a significant issue in emerging countries. The enormous rate of population growth, industrialization, and urbanization is responsible for these developments. Monitoring and mapping of changes in land cover and land use is essential to the sustainable development and management of the area. The study attempts to track changes in LULC pattern for the years 2002, 2013, and 2023 in the Hindon River Basin, a major tributary of the Yamuna River, using remote sensing and geographic information system techniques. Images obtained from Landsat data were employed to extract historical land use and land cover maps. Additionally, the CA-Markov model was implemented to forecast future land use and land cover patterns. This study examines the historical and predicted LULC in the area. Field observations and site-specific interviews were used to confirm and determine the ground realities. High-resolution images were used to evaluate the accuracy of the classified map. According to the results, the agricultural land decreased from 60.98% in 2002 to 54.70% in 2050, while built-up areas increased from 12.95% to 21.25% during the same period. By 2050, vegetation is predicted to increase to 2.58%, whereas surface water, fallow land, barren areas, and dry water bodies are predicted to decrease to 0.58%, 18.87%, 1.20%, and 0.83%, respectively. The rapid pace of urbanization is facilitating economic growth within the country; however, this development is occurring at the expense of the natural landscape, which subsequently diminishes the overall quality of human life. In order to maintain sustainable development in the Hindon Basin, proper urban planning is essential. Important policy implications for the sustainable management of land use and conservation in the Hindon River basin are highlighted by the study’s research and findings. Full article

The present study was focused on heavy metal distribution patterns and the associated ecological risk assessment in the adjoining sediment of the Hindon River in Muzaffarnagar Region (U.P.), India. Lead (Pb), zinc (Zn), copper (Cu), cadmium (Cd), nickel (Ni), iron (Fe), aluminum (Al), sodium (Na), and potassium (K) were estimated from six sediment samples (Atali A and B, Budhana A and B, and Titavi A and B). The concentration of the heavy metals Zn, Pb, Cu, Ni, and Cd ranged from 25.5–74.7 mg kg⁻¹, 29.8–40.6 mg kg⁻¹, 7.0–29.2 mg kg⁻¹, 14.7–21.8 mg kg⁻¹, and 0.96–1.2 mg kg⁻¹, respectively and followed the sequence Zn > Pb > Cu > Ni > Cd, while major elements followed the sequence Na > Fe > Al > K. The enrichment factor (EF) and geo-accumulation index (I_geo) revealed that Atali A showed the highest enrichment and followed the sequence Zn > Cu > Pb > Ni > Cd. Contamination factor (CF) and contamination degree (CD) depicted that all of the sites (except Titavi B) were moderately to considerably contaminated. The highest degree of contamination (CF, CD, and PLI, pollution load index) was observed at Titavi A followed by Atali A and Budhana A. Eco-toxicological risk assessment (RI) indicated that the sites were moderately contaminated, predominantly by Ni and Pb and Zn. The results revealed that the metal contamination in sediment is alarming and might pose an adverse threat to ecosystem health. Full article

Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GW_R), groundwater discharge (GW_D), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management. Full article