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Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models
Hydrological modelling can be complex in nonhomogeneous catchments with diverse
geological, climatic, and topographic conditions. In this study, an integrated conceptual …
geological, climatic, and topographic conditions. In this study, an integrated conceptual …
[HTML][HTML] A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments
Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers
on water resources, essential for water resources management. The present study aims to …
on water resources, essential for water resources management. The present study aims to …
A hybrid wavelet–machine learning model for qanat water flow prediction
In many parts of semiarid and arid regions, qanats are the leading supplier of water demand
for agricultural and drinking usage. Qanat is an ancient collecting water system, and qanat …
for agricultural and drinking usage. Qanat is an ancient collecting water system, and qanat …
A novel scheme for the hyperbolic partial differential equation through Fibonacci wavelets
In this study, we generated the operational matrices of integration based on the Fibonacci
wavelets through the concept of linear algebra and developed the novel technique known …
wavelets through the concept of linear algebra and developed the novel technique known …
[HTML][HTML] B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology
In this manuscript, we present B-AMA (Basic dAta-driven Models for All), an easy, flexible,
fully coded Python-written protocol for the application of data-driven models (DDM) in …
fully coded Python-written protocol for the application of data-driven models (DDM) in …
Hourly rainfall-runoff modelling by combining the conceptual model with machine learning models in mostly karst Ljubljanica River catchment in Slovenia
Hydrological modelling, essential for water resources management, can be very complex in
karst catchments with different climatic and geologic characteristics. In this study, three …
karst catchments with different climatic and geologic characteristics. In this study, three …
[HTML][HTML] Advanced Bio-Inspired computing paradigm for nonlinear smoking model
Smoking has emerged as one of the leading global factors that is the source of health
issues. It damages almost all of the body's organs. It damages various muscles and causes …
issues. It damages almost all of the body's organs. It damages various muscles and causes …
Interpretable spatial-temporal attention convolutional network for rainfall forecasting
The interpretability of rainfall forecasting models is a major challenge in the field of artificial
intelligence. Its importance is equal to the evaluation of model accuracy. Owing to the …
intelligence. Its importance is equal to the evaluation of model accuracy. Owing to the …
Real-time flood forecasting using satellite precipitation product and machine learning approach in Bagmati river basin, India
Real-time flood forecasting is crucial for early flood warnings. It relies on real-time
hydrological and meteorological data. Satellite Precipitation Products offer real-time global …
hydrological and meteorological data. Satellite Precipitation Products offer real-time global …
Analysing of rainfall-runoff modelling using a hybrid DNN-SGD optimisation in Sub Basin of Brahmaputra River, India
S Sinha - International Journal of Hydrology Science and …, 2025 - inderscienceonline.com
The main objective of this research is to improve the accuracy of runoff prediction and
assess the effectiveness of the proposed DNN-SGD model. The performance of the DNN …
assess the effectiveness of the proposed DNN-SGD model. The performance of the DNN …