Browsing by Author "Rajput J."
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Item A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration(Nature Research, 2024) Joshi B.; Singh V.K.; Vishwakarma D.K.; Ghorbani M.A.; Kim S.; Gupta S.; Chandola V.K.; Rajput J.; Chung I.-M.; Yadav K.K.; Mirzania E.; Al-Ansari N.; Mattar M.A.Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and�used�to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott�s index of agreement (WI), and Legates�McCabe�s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02�mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71�mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships. � The Author(s) 2024.Item Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of�Bino watershed, Uttarakhand(Springer, 2024) Tulla P.S.; Kumar P.; Vishwakarma D.K.; Kumar R.; Kuriqi A.; Kushwaha N.L.; Rajput J.; Srivastava A.; Pham Q.B.; Panda K.C.; Kisi O.Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (i.e., Bino watershed, Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000�2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as�the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains. � The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.Item Microbes and compost: an emerging role in climate resilience agriculture(Elsevier, 2024) Rai A.; Mishra R.; Patra A.; Singh A.K.; Sharma S.; Arvind A.; Bahuguna A.; Rai S.; Rajput J.; Gangwar A.; Jha S.; Tripathi S.K.; Srivastava R.K.; Shekhar D.; Singh S.K.; Kapil T.; Sharma R.B.; Rai S.Microbes and their metabolic activity are crucial for a healthy and functioning soil. The rhizosphere, where plant roots and microbes mingle, is a bustling hub for nutrient cycling, energy flow, and microbial activity. Sustainable farming prioritizes nurturing these rhizospheric processes. Biofertilizers, including symbiotic and nonsymbiotic microbial partnerships, plant growth-promoting microbes, and arbuscular mycorrhizal collaborations, all play diverse roles in soil health and plant growth. Some microbes like Pseudomonas spp., Bacillus spp., and Streptomyces spp. help convert insoluble phosphorus into plant available forms. Composting, is another sustainable process, transforms organic waste into valuable compost, a dual-action fertilizer and soil amendment. Microbes decompose organic matter in compost, turning it into a stable, plant-friendly material. This aerobic process breaks down easy-to-digest molecules, generating CO2 and more durable substances. Composting effectively manages organic waste, reusing nutrients, reducing volume and moisture, and breaking down harmful organics plus, intricate humic-like chemicals form, boosting soil health. Thus, understanding and nurturing the vibrant microbial world in the rhizosphere through sustainable practices like biofertilizers and composting is key to healthy soil and a thriving future for farming. � 2025 Elsevier Inc. All rights reserved.Item Nexus between nanotechnology and agricultural production systems: challenges and future prospects(Springer Nature, 2024) Rana L.; Kumar M.; Rajput J.; Kumar N.; Sow S.; Kumar S.; Kumar A.; Singh S.N.; Jha C.K.; Singh A.K.; Ranjan S.; Sahoo R.; Samanta D.; Nath D.; Panday R.; Raigar B.L.Sustainable agriculture is crucial for meeting the growing global food demand. With the pressure of climate change, resource depletion, and the need for increased agricultural productivity, innovative approaches are essential. Nanotechnology is an emerging technology in achieving sustainable development goals (SDGs). Despite its promising benefits, the safe implementation of nanotechnology in agriculture requires careful consideration of potential health and environmental risks. However, there is a lack of comprehensive documentation on the application, potential and limitations of nanotechnology in the field of agriculture. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 157 initially identified articles, 85 were deemed pertinent, focusing primarily on potential nanotechnology in smart agricultural systems. Taking into account research findings worldwide, we found significant improvements with nanotechnology over traditional methods which underscores the practical benefits of nanotechnology, including increased crop yields, efficient resource use, and reduced environmental footprint. The objective of this systematic review is to explore the nexus between nanotechnology and agricultural systems, highlighting its potential to enhance productivity, sustainability, and resilience and to inform researchers, practitioners, and policymakers about the transformative impact of nanotechnology on sustainable agriculture and underscores the need for further research to address safety concerns and maximize its potential for agricultural advancement. � The Author(s) 2024.
