A5016408387- Hydrological Forecasting Using AI
- Hydrology and Watershed Management Studies
- Energy Load and Power Forecasting
- Solar Radiation and Photovoltaics
- Metaheuristic Optimization Algorithms Research
- Climate variability and models
- Meteorological Phenomena and Simulations
- Hydrology and Drought Analysis
- Heat Transfer Mechanisms
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
- Heat Transfer and Optimization
- Chaos-based Image/Signal Encryption
- Structural Health Monitoring Techniques
- Advanced Multi-Objective Optimization Algorithms
- Infrastructure Maintenance and Monitoring
- Evolutionary Algorithms and Applications
- Smart Grid Energy Management
- Plant Water Relations and Carbon Dynamics
- Hydrology and Sediment Transport Processes
- Neural Networks and Applications
- Water Quality Monitoring Technologies
- Flood Risk Assessment and Management
- Cryptographic Implementations and Security
- Nanofluid Flow and Heat Transfer
Northern Technical University
2024
Ahl Al Bayt University
2024
Imam Ja’afar Al-Sadiq University
2022-2023
Bayan College
2023
Imam Sadiq University
2022-2023
Dijlah University College
2020-2022
University of Kerbala
2022
University of Warith Al-Anbiyaa
2022
Duy Tan University
2019-2021
University of Anbar
2018-2020
Streamflow forecasting is essential for hydrological engineering. In accordance with the advancement of computer aids in this field, various machine learning (ML) models have been explored to solve highly non-stationary, stochastic, and nonlinear problem. current research, a newly version an ML model called long short-term memory (LSTM) was investigated streamflow prediction using historical data particular period. For case study located tropical environment, Kelantan river northeast region...
Project delays are the major problems tackled by construction sector owing to associated complexity and uncertainty in activities. Artificial Intelligence (AI) models have evidenced their capacity solve dynamic, uncertain complex tasks. The aim of this current study is develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources factors identified. A questionnaire...
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...
Evapotranspiration is one of the most important components hydrological cycle as it accounts for more than two-thirds global precipitation losses. Indeed, accurate prediction reference evapotranspiration (ETo) highly significant many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation ETo a major concern in hydrology. can be estimated using different approaches, field measurement, empirical formulation...
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...
This study aims to develop an adaptive mesh refinement (AMR) algorithm combined with Cut-Cell IBM using two-stage pressure–velocity corrections for thin-object FSI problems. To achieve the objective of this study, AMR-immersed boundary method (AMR-IBM) discretizes and solves equations motion flow that involves rigid thin structures layer at interface between structure fluid. The body forces are computed in proportion fraction solid volume fluid cells incorporate motions into boundary....
Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that facing engineers. These due the complex processes associated with several natural variables such non-stationarity, non-linearity, randomness. In this study, a new model proposed predict long-term streamflow. Several lags cover years abstracted using potential of Extreme Gradient Boosting (XGB) then after selected inputs...
The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow Pahang River, located in a tropical climatic region Peninsular Malaysia. Three different optimization algorithms namely particle swarm (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune membership function ANFIS model order improve capability forecasting. Different combination antecedent was develop...
Evaporation, one of the fundamental components hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction evaporation essential for multiple water resources engineering applications, particularly developing countries like Iraq where stations are not sustained and operated appropriately situ estimations. This advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research,...
The processes of retrieving useful information from a dataset are an important data mining technique that is commonly applied, known as Data Clustering. Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, clustering problem particular. Black Hole (BH) algorithm has underlined solution problems, which it population-based metaheuristic emulates phenomenon black holes universe. In this instance, every motion within search space...
Accurate solar radiation (SR) prediction is one of the essential prerequisites harvesting energy. The current study proposed a novel intelligence model through hybridization Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization (GOA) (ANFIS-muSG) for global SR at different locations North Dakota, USA. performance ANFIS-muSG was compared classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf...
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...
Abstract In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a highly essential several applications in the field of water resources engineering. One main contributors modeling reliability optimization input variables to achieve model. The step selection proper combinations. Hence, developing algorithm that can determine optimal combinations crucial. This study introduces Genetic (GA) better combination...
In this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves economical operation of electrical power system reducing environmental pollution produced by conventional generation. Here, Combined Economic Emission Dispatch (CEED) problem represented using a max/max Price Penalty Factor (PPF) confine system's nonlinearity. PPF considered transform four-objective into single-objective optimization...