Genetic programming in water resources engineering: A state-of-the-art review
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …
automatic generation of computer programs. In recent decades, GP has been frequently …
Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning
Monthly rainfall prediction is a crucial topic for the management of water resources and
prevention of hydrological disasters. To make a multi-step monthly rainfall prediction and …
prevention of hydrological disasters. To make a multi-step monthly rainfall prediction and …
A review of the application of hybrid machine learning models to improve rainfall prediction
Rainfall is one of the most important meteorological phenomena that impacts many fields,
including agriculture, energy, water resources management, and mining, among others …
including agriculture, energy, water resources management, and mining, among others …
Forecasting monthly precipitation using sequential modelling
In the hydrological cycle, rainfall is a major component and plays a vital role in planning and
managing water resources. In this study, new generation deep learning models, recurrent …
managing water resources. In this study, new generation deep learning models, recurrent …
Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation
F Ahmadi, S Mehdizadeh, B Mohammadi… - Agricultural Water …, 2021 - Elsevier
Reference evapotranspiration (ET 0) is one of the most important parameters, which is
required in many fields such as hydrological, agricultural, and climatological studies …
required in many fields such as hydrological, agricultural, and climatological studies …
Developing novel robust models to improve the accuracy of daily streamflow modeling
B Mohammadi, F Ahmadi, S Mehdizadeh… - Water Resources …, 2020 - Springer
Streamflow plays a major role in the optimal management and allocation of available water
resources in each region. Reliable techniques are therefore needed to be developed for …
resources in each region. Reliable techniques are therefore needed to be developed for …
Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models
F Fathian, S Mehdizadeh, AK Sales, MJS Safari - Journal of Hydrology, 2019 - Elsevier
Prediction of river flow as a fundamental source of hydrological information plays a crucial
role in various fields of water projects. In this study, at first, the capabilities of two time series …
role in various fields of water projects. In this study, at first, the capabilities of two time series …
Comparison of artificial intelligence methods in estimation of daily global solar radiation
Assessment of solar potential over a location of interest is introduced as an important step
for the successful planning of solar energy systems (photovoltaic or thermal). Due to the …
for the successful planning of solar energy systems (photovoltaic or thermal). Due to the …
Drought modeling using classic time series and hybrid wavelet-gene expression programming models
The standardized precipitation evapotranspiration index (SPEI) at three different time scales
(ie, SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are …
(ie, SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are …
Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate
A novel hybrid approach is presented that can more accurately predict monthly rainfall in a
tropical climate by integrating a linear stochastic model with a powerful non-linear extreme …
tropical climate by integrating a linear stochastic model with a powerful non-linear extreme …