Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review
Hydrological processes forecasting is an essential step for better water management and
sustainability. Among several hydrological processes, lake water level (LWL) forecasting is …
sustainability. Among several hydrological processes, lake water level (LWL) forecasting is …
Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams
Discharge of pollution loads into natural water systems remains a global challenge that
threatens water and food supply, as well as endangering ecosystem services. Natural …
threatens water and food supply, as well as endangering ecosystem services. Natural …
Modeling various drought time scales via a merged artificial neural network with a firefly algorithm
B Mohammadi - Hydrology, 2023 - mdpi.com
Drought monitoring and prediction have important roles in various aspects of hydrological
studies. In the current research, the standardized precipitation index (SPI) was monitored …
studies. In the current research, the standardized precipitation index (SPI) was monitored …
Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India
Agricultural droughts are a prime concern for economies worldwide as they negatively
impact the productivity of rain-fed crops, employment, and income per capita. In this study …
impact the productivity of rain-fed crops, employment, and income per capita. In this study …
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR …
Agriculture, meteorological, and hydrological drought is a natural hazard which affects
ecosystems in the central India of Maharashtra state. Due to limited historical data for …
ecosystems in the central India of Maharashtra state. Due to limited historical data for …
[HTML][HTML] Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model
In the current study, the Standardized Precipitation Evaporation Index (SPEI) was forecasted
using a combination of the empirical wavelet transform (EWT), discrete wavelet transforms …
using a combination of the empirical wavelet transform (EWT), discrete wavelet transforms …
[HTML][HTML] Long-term trend analysis of extreme climate in Sarawak tropical peatland under the influence of climate change
Extreme climate is one of the important variables which determine the capability of tropical
peatland to act as either carbon sink and/or carbon source. The purpose of this study is to …
peatland to act as either carbon sink and/or carbon source. The purpose of this study is to …
Evaluation of machine learning techniques for hydrological drought modeling: A case study of the Wadi Ouahrane basin in Algeria
Forecasting meteorological and hydrological drought using standardized metrics of rainfall
and runoff (SPI/SRI) is critical for the long-term planning and management of water …
and runoff (SPI/SRI) is critical for the long-term planning and management of water …
Comparative assessment of improved SVM method under different kernel functions for predicting multi-scale drought index
This paper focus on the drought monitoring and forecasting for semi-arid region based on
the various machine learning models and SPI index. Drought phenomena are crucial role in …
the various machine learning models and SPI index. Drought phenomena are crucial role in …
Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India
Standardized precipitation index prediction and monitoring are essential to mitigating the
effect of drought actions on precision farming, environments, climate-smart agriculture, and …
effect of drought actions on precision farming, environments, climate-smart agriculture, and …