Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms
Drought is considered one of the costliest natural disasters that result in water scarcity and
crop damage almost every year. Drought monitoring and forecasting are essential for the …
crop damage almost every year. Drought monitoring and forecasting are essential for the …
The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
This paper focuses on the development of a robust accurate streamflow prediction model by
balancing the abilities of exploitation and exploration to find the best parameters of a …
balancing the abilities of exploitation and exploration to find the best parameters of a …
Modeling of osmotically-driven membrane processes: An overview
In the modern era, mathematical modeling is a promising tool to evaluate the forward
osmosis (FO) and pressure retarded osmosis (PRO) systems process feasibility and check …
osmosis (FO) and pressure retarded osmosis (PRO) systems process feasibility and check …
Adaptive neuro-fuzzy inference system coupled with shuffled frog lea** algorithm for predicting river streamflow time series
Accurate runoff forecasting plays a key role in catchment water management and water
resources system planning. To improve the prediction accuracy, one needs to strive to …
resources system planning. To improve the prediction accuracy, one needs to strive to …
Artificial neural network optimized with a genetic algorithm for seasonal groundwater table depth prediction in Uttar Pradesh, India
Accurate information about groundwater level prediction is crucial for effective planning and
management of groundwater resources. In the present study, the Artificial Neural Network …
management of groundwater resources. In the present study, the Artificial Neural Network …
Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model
The potential of several predictive models including multiple model-artificial neural network
(MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM) …
(MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM) …
Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: Case study of Fontaine Gazelles Dam …
Seepage flow through embankment dam is one of the most influential factors in failures of
them. Thus, the monitoring and accurate measuring of seepage are crucial for the safety and …
them. Thus, the monitoring and accurate measuring of seepage are crucial for the safety and …
Estimation of total dissolved solids (TDS) using new hybrid machine learning models
The overall quality of Groundwater (GW) is important, primarily because it determines the
suitability of water for drinking, irrigation, and domestic purposes. In this study, the adaptive …
suitability of water for drinking, irrigation, and domestic purposes. In this study, the adaptive …
Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration
Accurate estimation of reference evapotranspiration (ET o) is profoundly crucial in crop
modeling, sustainable management, hydrological water simulation, and irrigation …
modeling, sustainable management, hydrological water simulation, and irrigation …
A survey towards decision support system on smart irrigation scheduling using machine learning approaches
From last decade, Big data analytics and machine learning is a hotspot research area in the
domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big …
domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big …