Advanced Hydrological Simulation and Hybrid CNN-LSTM Models for Sustainable Water Resource Management in Nepal

Nepal's diversified terrain and vast watershed create both opportunities and obstacles for water resource management. Conventional methods frequently fail owing to insufficient data, old methodologies, plus a lack of compatibility with modern technology. To solve these issues, the research suggests using improved hydrological simulation and sophisticated models for forecasting for sustainable water resource management in Nepal. The study uses cutting-edge technology, such as satellite imagery, Geographic Information Systems (GIS), with neural networks, to create a new combination CNN-LSTM technique. Hydrological statistics regarding precipitation, river flow, and glacial melt, as well as meteorological, geographical, and socioeconomic information, was gathered then filtered. This data integration enhances the accuracy of models, providing real-time monitoring and prediction capabilities. The hybrid CNN-LSTM system incorporates the qualities of Convolutional Neural Networks with collecting time properties from images, CatBoost for dealing with tabular information and spatial traits, with LSTM for enhanced classification of images and sequence data processing. The suggested model beats previous techniques, with an estimation accuracy of 99.20% to identify hydrologic occurrences. The incorporation of these latest innovations enhances floods projections, famine projections, and general handling of water techniques. This study shows that sophisticated hydrological simulations may considerably improve the durability and long-term viability of the management of water resources in Nepal, making it a valuable tool for regulators and managers. The research results call for bigger investments in the internet, training, and the creation of favorable regulations to assure the long-term sustainability of these programs. By tackling the constraints of existing approaches and adopting creative approaches, Nepal may accomplish responsible management of water resources while ensuring the availability of water and increasing the standard of life over its people.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-04-03
DOI
https://doi.org/10.52783/jisem.v10i31s.5059
Citations
1
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
0.80
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article

Advanced Hydrological Simulation and Hybrid CNN-LSTM Models for Sustainable Water Resource Management in Nepal

Manan Sharma
1 citations
Journal of Information Systems Engineering & Management
Hydrological Forecasting Using AI
0.80
article

Advanced Hydrological Simulation and Hybrid CNN-LSTM Models for Sustainable Water Resource Management in Nepal

Manan Sharma
article en
1 citations

Abstract

Nepal's diversified terrain and vast watershed create both opportunities and obstacles for water resource management. Conventional methods frequently fail owing to insufficient data, old methodologies, plus a lack of compatibility with modern technology. To solve these issues, the research suggests using improved hydrological simulation and sophisticated models for forecasting for sustainable water resource management in Nepal. The study uses cutting-edge technology, such as satellite imagery, Geographic Information Systems (GIS), with neural networks, to create a new combination CNN-LSTM technique. Hydrological statistics regarding precipitation, river flow, and glacial melt, as well as meteorological, geographical, and socioeconomic information, was gathered then filtered. This data integration enhances the accuracy of models, providing real-time monitoring and prediction capabilities. The hybrid CNN-LSTM system incorporates the qualities of Convolutional Neural Networks with collecting time properties from images, CatBoost for dealing with tabular information and spatial traits, with LSTM for enhanced classification of images and sequence data processing. The suggested model beats previous techniques, with an estimation accuracy of 99.20% to identify hydrologic occurrences. The incorporation of these latest innovations enhances floods projections, famine projections, and general handling of water techniques. This study shows that sophisticated hydrological simulations may considerably improve the durability and long-term viability of the management of water resources in Nepal, making it a valuable tool for regulators and managers. The research results call for bigger investments in the internet, training, and the creation of favorable regulations to assure the long-term sustainability of these programs. By tackling the constraints of existing approaches and adopting creative approaches, Nepal may accomplish responsible management of water resources while ensuring the availability of water and increasing the standard of life over its people.

Journal of Information Systems Engineering & ManagementVol. 10(31s)
Openalex Percentile: Top 31%
Hydrological Forecasting Using AI
0.80
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