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Groundwater level estimation using improved deep learning and soft computing methods
Estimating groundwater level (GWL) is an important issue for planning and managing available water resources. This study uses monthly data from 86...
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Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data
This study compares the ability of Long Short-Term Memory (LSTM) tuned with Grey Wolf Optimization (GWO) and machine learning models, artificial...
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Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks
Accurate and reliable monthly runoff forecasting plays an important role in making full use of water resources. In recent years, long short-term...
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Double-index rainfall warning and probabilistic physically based model for fast-moving landslide hazard analysis in subtropical-typhoon area
In subtropical typhoon-prone regions, landslides are triggered by short-duration intense rainfall and prolonged periods of elevated pore-water...
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Machine Learning Techniques to Predict Rock Strength Parameters
To accurately estimate the rock shear strength parameters of cohesion ( C ) and friction angle ( φ ), triaxial tests must be carried out at different...
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Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions
Pan evaporation modeling and forecasting are needed to provide timely, continuous, and valuable information to support water management. This study...
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Development of Advanced Data-Intelligence Models for Radial Gate Discharge Coefficient Prediction: Modeling Different Flow Scenarios
This research aims to predict a radial gate's discharge coefficient (C d ) under free and submerged flow conditions using several machine learning (ML)...
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A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting
Reservoir modeling and inflow forecasting has a vital role in water resource management/controlling. Hydrological systems’ complex nature and...
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A combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting
Water shortage is a major problem facing the world. Artificial precipitation enhancement is an effective way to improve precipitation conversion...
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Investigating hybrid deep learning models and meta-heuristic algorithms in predicting evaporation from a reservoir: a case study of Dez dam
Reservoirs are crucial for water storage, flood control, and electricity generation. At the same time, evaporation in dam reservoirs causes water...
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Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm
River discharge represents critical hydrological data that can be used to monitor the hydrological status of a river basin. The objective of this...
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Enhanced variational mode decomposition with deep learning SVM kernels for river streamflow forecasting
The present scenario of global climatic change challenges the sustainability and existence of water bodies around the globe. Due to which, it is...
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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters
With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary...
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An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting
Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting...
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A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model
Accurate forecasting of mid to long-term runoff is essential for water resources management. However, the traditional model cannot predict well and...
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Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin
Predicting rainfall amount is essential in water resources planning and for managing structures, especially those against floods and long-term...
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Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm
The hydropower industry is one of the most important sources of clean energy. Predicting hydropower production is essential for the hydropower...
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Application of novel binary optimized machine learning models for monthly streamflow prediction
Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation...
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Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin
The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct...
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Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique
Accurate prediction of monthly runoff is critical for optimal water resource allocation. However, previous studies mainly focused on the direct...
