Wyniki wyszukiwania - Biblioteka Nauki

1

Analysis of hydrological alteration and environmental flow in Sone river basin

100%

Ranjan A. , Roshni T.

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2023

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tom Vol. 71, no. 2

949--960

EN

Environmental flow is an important indicator of river health as it maintains the natural flow pattern of riverine ecosystem. Although numerous researches for analyzing the hydrological alterations are there, still insightful investigation of site specific knowledge should be required for riverine ecosystem protection. In this study, the objective is to analyze the hydrological status of the Sone river basin in Bihar region, India. This study also focuses to develop a flow duration curve (FDC) to show the time duration–frequency of low-flow events. The hydrological status of the basin was analyzed using indicators of hydrologic alteration (IHA). Low flows were estimated using period of record flow duration curve (POR FDC), and design environmental flow was assessed for 10-year and 100-year return period using stochastic flow duration curve (stochastic FDC). Daily discharge data collected from Koelwar station of Sone river for 1990–2020 period were used for the hydrological analysis. Depending on the quantitative and qualitative assessment of the hydrological alterations, it was found that the hydrological status of the river basin is in a "very altered" state. The POR FDC analyzed 7-day mean discharge values (7dQ) appropriate for determining low flows, and discharge values corresponding to 95% probability of exceedance (Q95) were considered as low flow for 7dQ. Stochastic FDCs generated 7-day mean flow duration curves for 10-year (7Q10) and 100-year (7Q100) recurrence intervals. Discharge values corresponding to 95% probability of exceedance for 7Q10 range from 120 to 125 cumec and those for 7Q100 range from 135 to 140 cumec. The methodology proposed in this work to design environmental flow considering the effects of hydrological alteration can help in making the long-term strategies to protect the riverine ecosystem in Sone river basin.

2

Selection of level and type of decomposition in predicting suspended sediment load using wavelet neural network

80%

Reddy B. S. N. , Pramada S. K. , Roshni T.

Acta Geophysica

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2022

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tom Vol. 70, no 2

847--857

EN

The advancements in artificial intelligence play a significant role in solving the problems of researchers and engineers to develop prediction models with higher accuracy over the analytical and numerical models. The wavelet ensemble artificial intelligence model has a widespread application in forecasting hydrological datasets. The signal decomposition type, level and the mother wavelet affect the model performance in wavelet-based approaches. The present analysis focuses on studying the significance of the level and type of decomposition in wavelet transform for pre-processing the input variables to predict the target variable. In this work, to forecast seasonal suspended sediment load of the Kallada River basin in Kerala, two types of decomposition with decomposition levels ranging from 2 to 7 were adopted using seasonal flow data (wet and dry seasons). To rank the WANN models, compromise programming was adopted using the results based on statistical performance indicators and compared with the performance of the conventional FFNN model. From the accuracy assessment and ranking, type-2 with 5th level decomposition can capture the actual periodicity of the signal and predict the suspended sediment load with higher accuracy. It also shows the capability to predict the extreme events of time series.

3

Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level

70%

Roshni T. , Mirzania E. , Hasanpour Kashani M. , Bui Q.-A. T. , Shamshirband S.

Acta Geophysica

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2022

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tom Vol. 70, no 4

1885--1898

EN

Groundwater level time series is a prime factor for variety of groundwater studies and is of great significance for the management of groundwater resources. Quality control of groundwater level observations is essential for hydrological applications. Artificial Intelligent techniques deal with highly nonlinear interactions and complex hydrological process and hence can be a better alternative for groundwater level prediction. In this research, the performances of Support Vector Regression (SVR) and SVR ensembled with metaheuristic Algorithm of Innovative Gunner (AIG) models were evaluated in simulating the monthly groundwater level of the Shabestar plain during the period 2001–2019. The 80 and 20% of the monthly dataset were used for training and testing the developed models. The efficiency of the developed models was compared using different statistical indices including correlation coefficient (R), Nash–Sutcliffe Efficiency (NSE) coefficient, Root-Mean-Square Error (RMSE), RMSE-observation standard deviation ratio (RSR) and Legates & McCabe’s Index (ELM). The results showed that the hybrid model (SVR-AIG) generates accurate estimations in combinatory patterns. Moreover, among the SVR and SVR-AIG models with different input scenarios, the SVR-AIG model showed best results for scenario 6 (M6) in both the training stage (R=0.995, NSE=0.99, RMSE=0.151 (m), RSR=0.096 and ELM =0.916) and the testing stage (R=0.941, NSE=0.879, RMSE=0.146 (m), RSR=0.346 and ELM =0.660). The hybrid SVR-AIG model is shown to be more accurate and robust than the SVR models, providing a novel capability to capture unknown time-varying dependencies. In general, the results of the proposed model are promising and it provides a reliable insight for water resources planners in conducting future research of groundwater resources.