A5000307425- Hydrology and Watershed Management Studies
- Hydrological Forecasting Using AI
- Hydrology and Drought Analysis
- Climate variability and models
- Flood Risk Assessment and Management
- Hydraulic flow and structures
- Hydrology and Sediment Transport Processes
- Meteorological Phenomena and Simulations
- Climate change impacts on agriculture
- Precipitation Measurement and Analysis
- Water resources management and optimization
- Water Quality Monitoring Technologies
- Soil Moisture and Remote Sensing
- Dam Engineering and Safety
- Plant Water Relations and Carbon Dynamics
- Water Systems and Optimization
- Soil and Unsaturated Flow
- Solar Radiation and Photovoltaics
- Energy Load and Power Forecasting
- Soil erosion and sediment transport
- Groundwater and Isotope Geochemistry
- Water Quality and Pollution Assessment
- Geophysics and Gravity Measurements
- Neural Networks and Applications
- Reservoir Engineering and Simulation Methods
Islamic Azad University, Science and Research Branch
2018-2025
Al-Ayen University
2023-2025
Thi Qar University
2023-2024
Islamic Azad University, Tehran
2024
Duy Tan University
2020-2021
Iran University of Science and Technology
2014
We assessed the changes in meteorological drought severity and return periods during cropping seasons Afghanistan for period of 1901 to 2010. The droughts country were analyzed using standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall Climate Research Unit temperature data both at 0.5° resolutions used this purpose. Seasonal estimated values SPEI fitted with best distribution function. Trends climatic variables modified Mann–Kendal...
The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EPm) at two stations (e.g. Ranichauri Pantnagar) India. Monthly climatological information used for simulating evaporation. utmost effective input-variables MM-ANN, MGGP, MARS, SVM, M5Tree...
Abstract Iran has a predominantly arid and semi‐arid climate where the drought hazards their variability are crucial concerns for water resources management. This study assesses characteristics trends of meteorological droughts features in different regions Iran. Monthly rainfall data analysed using Standardized Precipitation Index (SPI) to reconstruct time steps (1, 3, 6, 9 12). The events decomposed into three features, namely Severity (S), Duration (D) Peak (P) assess spatial variations...
Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on operation dams and reservoir capacity. This investigation aimed at exploring new version machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), K Star (KS) for SSL prediction Trenton meteorological station Delaware River, USA. Different input scenarios were examined based flow...
Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble models, namely gradient boost regression (GBR), AdaBoost (ABR) and random forest (RFR) are proposed for of suspended sediment load (SSL), their performance related uncertainty assessed. The SSL the Mississippi River, which is one major world rivers significantly affected by sedimentation, predicted...
The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. LSSVM-BA model results are compared those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show efficacy this novel model. river water quality data at three monitoring stations located USA considered simulation DO concentration. Eight input combinations four...
Abstract A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists development of prediction models for mitigation impacts. Droughts are usually monitored using indices (DIs), most probabilistic therefore, highly stochastic non-linear. The current research investigated capability different versions relatively well-explored machine learning (ML) including random forest (RF), minimum probability regression...
Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future potentials. In this research, an evaluation preciseness extreme learning machine (ELM) model a fast and efficient framework for estimating global incident (G) is undertaken. Daily meteorological datasets suitable G estimation belongs northern parts Cheliff Basin in Northwest Algeria, used construct model. Cross-correlation functions...
Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, feasibility of Gamma test (GT) was investigated to extract optimal combination as primary step estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called co-active neuro-fuzzy inference system (CANFIS) developed EPm at Pantnagar station (located in Uttarakhand State) and Nagina Uttar Pradesh State), India. The proposed AI trained tested using...