Browsing by Author "Jitendra Rajput"

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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) Bhupendra Joshi; Vijay Kumar Singh; Dinesh Kumar Vishwakarma; Mohammad Ali Ghorbani; Sungwon Kim; Shivam Gupta; V.K. Chandola; Jitendra Rajput; Il-Moon Chung; Krishna Kumar Yadav; Ehsan Mirzania; Nadhir Al-Ansari; Mohamed A. Mattar
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.
Assessment of spatiotemporal variability of rainfall and surface wind speed over the eastern coastal agro-ecological zones of India using advanced trend detection approaches
(Springer, 2023) Pradosh Kumar Paramaguru; Kanhu Charan Panda; Truptimayee Suna; Jitendra Rajput
Rainfall and near-surface wind speed are two crucial parameters affecting climate change-induced extreme events. It is essential to perform a trend analysis of these parameters to assess the spatiotemporal variation of these events. Thus, the current study aimed to investigate seasonal rainfall and near-surface wind speed variability over six eastern coastal agro-ecological zones (AEZs) of India (AEZs 3, 7, 8, 11, 12, and 18) over 101 years (1920–2020). Further, the Mann–Kendall (MK), modified Mann–Kendall (MMK), bootstrapped Mann–Kendall (BMK), innovative trend analysis (ITA), and detrended fluctuation analysis (DFA) tests were employed in this study to analyse the trends of rainfall and near-surface wind speed. An increasing trend was noticed in the annual rainfall in the southern AEZs and the zones adjacent to the coastline. In the pre-monsoon season, the AEZs 12 and 18 showed an increasing rainfall trend, whereas the remaining AEZs demonstrated a decreasing trend. Except for AEZ 7, all zones experienced a negative rainfall trend in the post-monsoon season. The results revealed that all AEZs had negative near-surface wind speed trends, which could be attributed to climate change. ITA outperformed the rest of the trend analysis techniques in detecting hidden trends. The DFA test revealed that the trend pattern would continue in the future for 56% of the datasets. This study will assist researchers and policymakers in developing a sustainable water resources management plan by considering the trend patterns of meteorological variables across the agro-ecological regions. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
Conservation Agriculture for Soil Health and Carbon Sequestration in the Indian Himalayan Region
(Springer Singapore, 2023) Ashish Rai; Sumit Tripathi; Ayush Bahuguna; Sumit Rai; Jitendra Rajput; Anshu Gangwar; Rajeev Kumar Srivastava; Arvind Kumar Singh; Satish Kumar Singh; Dibyanshu Shekhar; Rahul Mishra; Eetela Sathyanarayana; Supriya Pandey
Mountains the most significant agro-ecosystems that directly or indirectly support human life. The areas surrounding the hills are abundant in biodiversity and have enormous potential for sustaining Indian agriculture. It has been widely recognised that the ecological fragility and sensitivity of the Himalayas to climatic aberrations, topography, peculiar geographical features, and some of the particular identified problems, which may be soil loss, runoff, steep slopes, acidity of soils, and loss of soil nutrients, form it a very distinct region as opposed to plains in terms of socioeconomic situation. Conventional agriculture was one of the best aspects of food production during the green revolution and after India gained its independence for securing food and nutrition through intensive agricultural practices, but on the flip side, it has simultaneous effects on resource degradation and soil biodiversity. The need for food and fodder, an ever-growing population, the preservation of soil biodiversity, declining soil health, climate change, the use of unbalanced fertilisers, and decreased farm profitability all call for a paradigm shift in the agriculture sector. On the other hand, increasing the intensity of the hillside agriculture system without implementing any conservation measures greatly increases the likelihood of disastrous conditions. Conservation agriculture has long been known to improve soil health and sustain agricultural production systems by reducing environmental footprints. Between the atmosphere and the lithosphere, numerous biological and physical processes are regulated by soils. An integral aspect of soil that promotes agricultural sustainability is soil health. However, each measurement of a specific soil health parameter is always tied to a unique set of circumstances. A fundamental concern in maintaining soil health to feed an expanding population is resource conservation. Climate change is a topic of discussion on a worldwide scale in the current globalisation context. The greenhouse effect is best for life but only up to a point beyond which it becomes dangerous. Due to urbanisation, changes in land use, cropping patterns, and other factors, human influences on climate change go beyond the range of natural fluctuation. Climate change in the soil system is significantly influenced by carbon regulation in the soil. The rate of organic matter decomposition is accelerated by an increase in mean annual temperature, which affects aggregate stability, water storage capacity, and nutrient balance— all of which are crucial for healthy soil structure, soil fertility, productivity, and sustainability. In actuality, soil bacteria break down organic materials, but a change in temperature regime may change the microbial population. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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) Paramjeet Singh Tulla; Pravendra Kumar; Dinesh Kumar Vishwakarma; Rohitashw Kumar; Alban Kuriqi; Nand Lal Kushwaha; Jitendra Rajput; Aman Srivastava; Quoc Bao Pham; Kanhu Charan Panda; Ozgur Kisi
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.
Microbes and compost: an emerging role in climate resilience agriculture
(Elsevier, 2024) Ashish Rai; Rahul Mishra; Abhik Patra; Arvind Kumar Singh; Sachin Sharma; A. Arvind; Ayush Bahuguna; Sumit Rai; Jitendra Rajput; Anshu Gangwar; Shankar Jha; Sumit Kumar Tripathi; Rajeev Kumar Srivastava; Dibyanshu Shekhar; Satish Kumar Singh; Tejaswini Kapil; Ram Babu Sharma; Supriya Rai
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.
Nexus between nanotechnology and agricultural production systems: challenges and future prospects
(Springer Nature, 2024) Lalita Rana; Manish Kumar; Jitendra Rajput; Navnit Kumar; Sumit Sow; Sarvesh Kumar; Anil Kumar; S.N. Singh; C.K. Jha; A.K. Singh; Shivani Ranjan; Ritwik Sahoo; Dinabandhu Samanta; Dibyajyoti Nath; Rakesh Panday; Babu Lal Raigar
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.