Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test
Abstract Knowledge of the stage-discharge rating curve is useful in designing and planning
flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental …
flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental …
Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation
The design and selection of ideal emitter discharge rates can be aided by accurate
information regarding the wetted soil pattern under surface drip irrigation. The current field …
information regarding the wetted soil pattern under surface drip irrigation. The current field …
Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms
The present research work focused on predicting the electrical conductivity (EC) of surface
water in the Upper Ganga basin using four machine learning algorithms: multilayer …
water in the Upper Ganga basin using four machine learning algorithms: multilayer …
Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed …
Water erosion creates adverse impacts on agricultural production, infrastructure, and water
quality across the world, especially in hilly areas. Regional-scale water erosion assessment …
quality across the world, especially in hilly areas. Regional-scale water erosion assessment …
Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin
Droughts are among the most hazardous and costly natural disasters and are hard to
quantify and characterize. Accurate drought forecasting reduces droughts' devastating …
quantify and characterize. Accurate drought forecasting reduces droughts' devastating …
Improving drought prediction accuracy: a hybrid EEMD and support vector machine approach with standardized precipitation index
This work combines the Support Vector Machine (SVM) model with Ensemble Empirical
Mode Decomposition (EEMD) to present a novel method for drought prediction. The EEMD …
Mode Decomposition (EEMD) to present a novel method for drought prediction. The EEMD …
Space–time heterogeneity of drought characteristics in Sabah and Sarawak, East Malaysia: implications for developing effective drought monitoring and mitigation …
Natural calamities like droughts have harmed not just humanity throughout history but also
the economy, food, agricultural production, flora, animal habitat, etc. A drought monitoring …
the economy, food, agricultural production, flora, animal habitat, etc. A drought monitoring …
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 aimed to overcome the …
valuable information to support water management. This study aimed to overcome the …
Hybrid river stage forecasting based on machine learning with empirical mode decomposition
The river stage is certainly an important indicator of how the water level fluctuates overtime.
Continuous control of the water stage can help build an early warning indicator of floods …
Continuous control of the water stage can help build an early warning indicator of floods …
[HTML][HTML] Characterizing Drought Prediction with Deep Learning: A Literature Review
A Márquez-Grajales, R Villegas-Vega… - MethodsX, 2024 - Elsevier
Drought prediction is a complex phenomenon that impacts human activities and the
environment. For this reason, predicting its behavior is crucial to mitigating such effects …
environment. For this reason, predicting its behavior is crucial to mitigating such effects …