Browsing by Author "V.K. Chandola"

Now showing 1 - 3 of 3

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.
Analysis of morphometric characteristics and prioritization of micro watersheds of Karamnasa River Basin using remote sensing & GIS technique
(Taylor and Francis Ltd., 2024) Sumit Kumar; Dhiraj Kumar; V.K. Chandola; Niraj Kumar Sonkar; Anuj Kumar Dwivedi; C.S.P. Ojha
The Karamnasa River, originating in Bihar’s Kaimur district, serves as a vital water resource in the region. This research, conducted at coordinates 25°30’54’‘N latitude and 83°52’30’’ E longitude, aimed to identify optimal sites for water conservation structures within the Karmanasa River Basin (KRS) using Remote Sensing and GIS techniques. By analysing morphometric parameters (MP) of the basin, including slope, stream length, and drainage density, the study delineated the basin into seven sub-watersheds and five stream orders. The comprehensive analysis revealed SW2 as the most critical sub-watershed, necessitating immediate conservation efforts. Assigning ranks to parameters like bifurcation ratio and circulatory ratio, SW2 emerged as the priority sub-watershed. Fifteen potential conservation sites were identified, comprising 9 farm ponds, 3 percolation tanks, and 3 check dams. The research underscores the significance of prioritizing sub-watersheds based on morphometric characteristics, with lower parameter values indicating higher priority. The study’s drainage network analysis, conducted through remote sensing and GIS, enhances understanding of the KRS hydrological features. This research highlights sustainable development through effective water resource use and targeted conservation in the Karamnasa River Basin, emphasizing community engagement and participatory methods for enduring, resilient environmental stewardship and successful land management. © 2024 Indian Society for Hydraulics.
Priority Assessment of Sub-watershed Based on Optimum Number of Parameters Using Fuzzy-AHP Decision Support System in the Environment of RS and GIS
(Springer, 2019) C.D. Mishra; R.K. Jaiswal; A.K. Nema; V.K. Chandola; Arpit Chouksey
Identification for planning of land and water resource management based on efficient decision-making tool is very important for providing appropriate weightage in stressed site. In the present study, fuzzy analytical hierarchy process (FAHP) with different erosion hazards parameters (EHPs) have been used as a pronouncement for identification of naturally stressed sub-watershed in Nagwan watershed of Hazaribagh district in Jharkhand, India. In fuzzy-AHP, analytical hierarchy process (AHP) builds a hierarchy (ranking) of decision items using comparisons between each pair of items expressed as a matrix with fuzziness. Paired comparisons produce weighting scores that measure how much importance items and criteria have with each other and checking the consistency of the decision. In this study, the Nagwan watershed was divided in 21 sub-watershed which varies from 2.34 to 7 km 2 and all EHPs of sub-watersheds have been computed using remote sensing and GIS. From the study, it has been observed that best consistency ratio has been found when using 13 parameters that is 9.44 with narrow trapezoidal shape. Each morphometric parameter was ranked with respect to the value and weightage obtained by deriving the relationships between the morphometric parameters obtained through classification of the SW by associating the strength of fuzzy analytical hierarchy processes (FAHP). By this weight, the results revealed that the priorities in five categories, out of 21 sub-watershed 19 and 24% sub-watersheds qualify for very high and high priority, whereas 57% sub-watersheds fall under medium, low and very low priority. © 2018, Indian Society of Remote Sensing.