JournalTOCs

International Journal of Hydrology Science and Technology
Journal Prestige (SJR): 0.43

Citation Impact (citeScore): 2
Number of Followers: 7

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2042-7808 - ISSN (Online) 2042-7816
Published by Inderscience Publishers

[439 journals]

Artificial neural network for modelling the sediments accumulation in
Es-Saada reservoir (North-Western Algeria)
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  Authors: Mustapha Sidi Adda, Djilali Yebdri, Djilali Baghdadi, Sarita Gajbhiye Meshram
  Pages: 1 - 16
  Abstract: Sediment deposition represents an important aspect of dam reservoir exploitation and management, as it relates to several operational and environmental problems. This study aimed to model the spatiotemporal evolution of the sediment accumulation in the Es-Saada reservoir (North-Western Algeria) using an artificial neural network (ANN) under low data conditions. The ANN model calibration was applied to the chronological period between the bathymetric surveys in 1986 and 2000, and the model verification was performed using data from a third survey conducted in 2003. The simulation of the reservoir bed presented acceptable results compared to the measured data (mean error of 7.76%). The model can provide predictive capacity curve for an average gap of 0.068 to the real curve, with a signification of 93.2%. It would be concluded that using determinist models for predicting sediment accumulation in reservoirs is complicated and needs all system details, while the application of ANN presents an adequate and uncomplicated method for predicting sediment distribution in dam reservoirs and also reservoir volume reduction in an approximate way.
  Keywords: sediments accumulation; artificial neural network; ANN; sedimentation modelling; Es-Saada reservoir; sediment discharge; Algeria
  Citation: International Journal of Hydrology Science and Technology, Vol. 17, No. 1 (2024) pp. 1 - 16
  PubDate: 2023-12-01T23:20:50-05:00
  DOI: 10.1504/IJHST.2024.135122
  Issue No: Vol. 17, No. 1 (2023)
A framework for the evaluation of MRP complex precipitation in a CORDEX-SA
regional climate applied to REMO
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  Authors: Shashikant Verma, A.D. Prasad, Mani Kant Verma
  Pages: 17 - 45
  Abstract: In this study, rainfall patterns are depicted using 16 regional climate models of seasonal monsoon across the Mahanadi Reservoir Project (MRP) Complex region from 1980 to 2005. Bias correction and different statistical analyses were used to evaluate the model's degree of uncertainty and model performance with the relevant observations, respectively. The purpose of this study is to: 1) compare the capability of regional climate models (RCMs) in reproducing seasonal monsoons; 2) climate change impact in the near future (2021-2046), mid-future (2047-2072), and far-future (2073-2098) over the study area. The seasonal monsoon rainfall under two different RCPs (RCP 4.5 and 8.5) was used to test the experiments and data's ability. Among 16 Coordinated Regional Climatic Downscaling Experiment (CORDEX) models, the REgional MOdel (REMO 2009) has a higher R<SUP align="right">2</SUP> (i.e., 0.610). Therefore, such studies assist to analyse the impact of monsoon rainfall on different sectors and responding to climate change.
  Keywords: CORDEX-South Asia; MRP complex; regional climate model; RCM; bias correction; statistical analysis
  Citation: International Journal of Hydrology Science and Technology, Vol. 17, No. 1 (2024) pp. 17 - 45
  PubDate: 2023-12-01T23:20:50-05:00
  DOI: 10.1504/IJHST.2024.135125
  Issue No: Vol. 17, No. 1 (2023)
Probabilistic flood risk assessment using coupled hydrologic and
2D-hydraulic model in the Jhelum River, Northwest Himalayas
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  Authors: Saika Manzoor, Manzoor Ahmad Ahanger
  Pages: 46 - 74
  Abstract: Keeping in mind the susceptibility to flooding-related disasters in the past, the need was identified for flood risk assessment using recent modelling techniques in the data-scarce River Jhelum, India. This study conducted a preliminary rainfall frequency analysis to find the best fit statistical model for calculating peak flow rates for multiple return periods. A framework was followed for the spatio-temporal delineation of flood-prone areas by integrating the watershed modelling system (WMS), Hydrologic Engineering Center-Hydrologic Modelling system (HEC-HMS), and two-dimensional Hydrologic Engineering Center-River Analysis System (2-D HEC-RAS) for different return period design floods. The 2-D unsteady state flood modelling in HEC-RAS showed the river overflowing its flow path for all the return periods, with 55% of the Srinagar city inundated in the 100-year event. The simulated flood depths and velocity maps for every design flood scenario are shown. The 2-D simulations yielded encouraging results compared to the most recent flood event.
  Keywords: hydrologic modelling; 2D hydraulic modelling; floodplain delineation; 2D HEC-RAS; River Jhelum
  Citation: International Journal of Hydrology Science and Technology, Vol. 17, No. 1 (2024) pp. 46 - 74
  PubDate: 2023-12-01T23:20:50-05:00
  DOI: 10.1504/IJHST.2024.135133
  Issue No: Vol. 17, No. 1 (2023)
A software for water pollution treatment technology evaluation by
supporting customisable indicator systems for specific scenarios
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  Authors: Bo Song, Chen Chen, Rongrong Kou
  Pages: 75 - 90
  Abstract: Determining the best water pollution treatment technology (WPTT) is one of the biggest challenges in water pollution management. We proposed the point of 'specific evaluations for specific scenarios' to select appropriate technologies that can improve the efficiency of water pollution treatment. This point refers to establishing specific indicator systems for water pollution treatment technology evaluation (WPTTE) for specific scenarios. A software was developed to achieve 'specific evaluations for specific scenarios' by supporting the rapid construction of customised indicator systems. The functions of this software include data management, indicator system matching, visualising construction of indicator systems, comprehensive evaluation and graphic display. In addition, the software was demonstrated by an example of ammonia nitrogen wastewater treatment technology selection. The software can meet the demands of multi-scene and multi-role evaluation and make the establishment of indicator systems more straightforward and effective. The study provides a solid foundation for 'specific evaluations for specific scenarios'.
  Keywords: water pollution; treatment technology; technology evaluation; specific evaluations for specific scenarios; indicator system; analytic hierarchy process; AHP; evaluation software
  Citation: International Journal of Hydrology Science and Technology, Vol. 17, No. 1 (2024) pp. 75 - 90
  PubDate: 2023-12-01T23:20:50-05:00
  DOI: 10.1504/IJHST.2024.135140
  Issue No: Vol. 17, No. 1 (2023)
Application of deep learning algorithm in hydrometry
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  Authors: Mohammad Zakwan, Shaik A. Qadeer, Mohammed Yousuf Khan
  Pages: 91 - 100
  Abstract: Estimation of discharge in a river is an integral part of water resource engineering. In this regard, various artificial intelligence (AI) techniques have been employed to model the discharge ratings. The present work compares the performance of two neural networks namely, back propagation neural network (BPNN) and radial basis neural network (RBNN), to model the discharge rating. The estimated discharge was also compared with the discharge estimated using conventional method. Published data of two gauging station was used for the comparative analysis. It was observed that application of neural networks greatly improved the estimates of discharge as compared to conventional method. Application of artificial neural network (ANN) reduced the sum of square of error (SSE) by about 90% on an average. Maximum absolute error was reduced from 51.36 and 141.21 to 5.04 and 7.68 respectively for the two stations for RBNN when compared to conventional method during validation. Calibration results reveal that among the BPNN and RBNN, RBNN could model the ratings at both the stations, better than BPNN.
  Keywords: hydrometry; discharge; river; ratings; neural networks; back propagation neural network; BPNN; radial basis neural network; RBNN; artificial neural network; ANN; sum of square of error; SSE
  Citation: International Journal of Hydrology Science and Technology, Vol. 17, No. 1 (2024) pp. 91 - 100
  PubDate: 2023-12-01T23:20:50-05:00
  DOI: 10.1504/IJHST.2024.135135
  Issue No: Vol. 17, No. 1 (2023)
Nonlinear multiple regression analysis for predicting seasonal streamflow
using climate indices for New South Wales
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  Authors: Rijwana Esha, Monzur A. Imteaz
  Pages: 101 - 116
  Abstract: This paper presents development of streamflow prediction models with long-lead timescale using the Multiple Non-Linear Regression (MNLR) technique. Four major climate indices which were found to be influencing the streamflow of New South Wales (NSW) are used for this purpose. The developed models with all the possible combinations show good results in terms of Pearson correlation(r), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Willmott index of agreement (d). The outcomes of MNLR models are compared to the best models of Multiple Linear Regression (MLR) analysis. MNLR models are evident to outperform the MLR models in terms of Pearson correlation (r) values, confirming the non-linear relationship between seasonal streamflow and large-scale climate drivers. Though the correlation values are not very high, they are statistically significant. The correlations obtained varied from 0.38 to 0.53 during calibration period, while it improved during the validation period, ranging from 0.52 to 0.63.
  Keywords: multiple nonlinear regression; MNLR; multiple linear regression; MLR; climate indices; streamflow; seasonal forecast; New South Wales; NSW
  Citation: International Journal of Hydrology Science and Technology, Vol. 17, No. 1 (2024) pp. 101 - 116
  PubDate: 2023-12-01T23:20:50-05:00
  DOI: 10.1504/IJHST.2024.135186
  Issue No: Vol. 17, No. 1 (2023)