- DEPARTMENT_STAFF.QUALIFICATION
M. Tech. (Civil Engineering), B. Tech. (Civil Engineering)
- DEPARTMENT_STAFF.DESIGNATION
Assistant Professor
- DEPARTMENT_STAFF.THRUST_AREA
Hydraulic Structures
- DEPARTMENT_STAFF.ADDRESS
Civil Engineering Section, University Polytechnic, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, UP, 202002
- DEPARTMENT_STAFF.MOBILE
- DEPARTMENT_STAFF.EMAIL
mohddanish.bp@amu.ac.in
- DEPARTMENT_STAFF.TIME_TABLE
Mohd Danish works as an Assistant Professor in the Civil Engineering Section of University Polytechnic, Aligarh Muslim University. He graduated in Civil Engineering in 2012 and completed his Master of Technology degree in 2014 from the Department of Civil Engineering, Aligarh Muslim University (AMU). Currently, he is pursuing his PhD from Aligarh Muslim University. Since his graduation days, he was involved in research activities and published various research papers in reputed journals and international conferences. His field of interest is soft-computing techniques, artificial intelligence tools, scour prediction, computational fluid dynamics and flood frequency analysis. His teaching subjects are Open channel flow, Surveying, Hydraulic structures, Hydraulics, etc. Research papers have been published mainly in SCI, ESCI and Scopus indexed journals, including Elsevier, Springer, IWA publishing etc. He has also received two gold medals and published one patent.
Mr. Danish is also involved in the AICTE approval process of University Polytechnic.
ORCID ID:
- Derivation of unit hydrograph using genetic algorithm-based optimization model The surface runoff can be predicted using hydrographs, and hence, the hydrographs become a prerequisite in designing hydrologic structures. The concept of unit hydrograph have been used widely in the field of hydrology in the past. There are different methods for the derivation of unit hydrographs like the ordinate method, matrix method, and the method of linear programming. In this study, a Genetic Algorithm-based optimization model has been created to identify the ordinates of unit hydrograph [U] to obtain a unique solution and avoid the challenges connected with the inversion of [P]T[P] matrix. The excess rainfall and direct runoff data sets are used to create an objective function for this purpose. The sum of the squares of the difference between the observed and the simulated direct runoffs is used to get the objective function. The simulated direct runoff values can be computed using the convolution equation [P][U]?=?[Q]. The Genetic Algorithm is then used to minimize the objective function in order to discover the ordinates of the unit hydrograph while taking into account, respectively, the 80%, 10%, and 10% of the total population size for elitism, crossover, and mutation. The root-mean-squared error of predicted values for three datasets obtained from the literature has been computed as 0.0126, 5.108, and 5.292.
- Performance analysis of different ANN modelling techniques in discharge prediction of circular side orifice A side orifice is a mechanism integrated into one or both side walls of a canal to redirect or release water from the main channel, and it has numerous applications in environmental engineering and irrigation. This research paper evaluates different artificial neural network (ANN) modeling algorithms for the estimation of discharge of a circular side orifice in open channels under free flow conditions. Four training algorithm were compared, namely, Gradient Descent (ANN-GD), Levenberg–Marquardt (ANN-LM), Gradient-Descent with Momentum (GDM), and Gradient-Descent with Adaptive Learning (GDA). Among all the models developed for discharge prediction through a circular side orifice, the ANN-LM model, which employed the LM algorithm for optimization during the backpropagation process, had the best performance during both training and testing. The AARE, R, E, and RMSE values were 3.13, 0.9994, 0.9987, and 0.0005, respectively, during training and 4.43, 0.9976, 0.9952, and 0.0010, respectively, during testing. The predicted discharge from the ANN-LM model was compared to the discharge equation proposed in the literature, and the comparison revealed that the ANN-LM model reduced the error in predicted discharge by 50%.
- Optimal design of triangular side orifice using multi-objective optimization NSGA-II Triangular orifices are widely used in industrial and engineering applications, including fluid metering, flow control, and measurement. Predicting discharge through triangle orifices is critical for correct operation and design optimization in various industrial and engineering applications. Traditional approaches like empirical equations have accuracy and application restrictions, whereas computational fluid dynamics (CFD) simulations can be computationally costly. Alternatively, artificial neural networks (ANNs) have emerged as a successful solution for predicting discharge through orifices. They offer a dependable and efficient alternative to conventional techniques for estimating discharge coefficients, especially in intricate relationships between input parameters and discharge. In this study, ANN models were created to predict discharge through the triangle orifice and velocity at the downstream of the main channel, and their effectiveness was assessed by comparing the performance with the earlier models proposed by researchers. This paper also proposes a novel hybrid multi-objective optimization model (NSGA-II) that uses genetic algorithms to discover the best values for design parameters that maximize discharge and downstream velocity simultaneously.
- PREDICTION OF SCOUR DEPTH AT BRIDGE ABUTMENTS IN COHESIVE BED USING GENE EXPRESSION PROGRAMMING MOHD DANISH, 2014. PREDICTION OF SCOUR DEPTH AT BRIDGE ABUTMENTS IN COHESIVE BED USING GENE EXPRESSION PROGRAMMING.International Journal of Civil Engineering & Technology (IJCIET).Volume:5,Issue:11,Pages:25-32.
- Scour Prediction at Bridge Piers in Cohesive Bed Using Gene Expression Programming
Accurate and reliable estimation of the scour depth at a bridge pier is essential for the safe and economical design of the bridge foundation. The phenomenon of scour at the pier placed on sediments is extremely complex in nature. Only a limited number of studies have been reported on local scour around bridge piers in cohesive sediment mainly due to the fact that scour modeling in cohesive beds is relatively more complex than that in sandy beds. Recent research has made good progress in the development of data-driven technique based on artificial intelligence (AI). It has been reported that AI-based inductive modeling techniques are frequently used to model complex process due to their powerful and non-linear model structures and their increased capabilities to capture the cause and effect relationship of such complex processes. Gene Expression Programming (GEP) is one of the AI techniques that have emerged as a powerful tool in modeling complex phenomenon into simpler chromosomal architecture. This technique has been proved to be more accurate and much simpler than other AI tools. In the present study, an attempt has been made to implement GEP for the development of scour depth prediction model at bridge piers in cohesive sediments using laboratory data available in literature. The present study reveals that the performance of GEP is better than nonlinear regression model for the prediction of scour depth at piers in cohesive beds.
?
- APPLICATION OF GENE EXPRESSION PROGRAMMING IN FLOOD FREQUENCY ANALYSIS JOURNAL OF INDIAN WATER RESOURCES SOCIETY
- A SOFTCOMPUTING APPROACH TO FLOOD FREQUENCY ANALYSIS OF RIVER GANGA
22nd International Conference on Hydraulics, Water Resources & Coastal Engineering
- SCOUR DEPTH PREDICTION AT BRIDGE ABUTMENTS IN COHESIVE BED USING GROUP METHOD OF DATA HANDLING
International Conference on Hydraulics, Water Resources and Coastal Engineering (Hydro2016)
- River Kosi, Sorrow of India: An overview
- Discharge coefficient estimation for rectangular side weir using GEP and GMDH methods
Flow through the rectangular side weir is a spatially varied type flow with decreasing discharge and used as a flow diversion structure. They are mainly used in the field of hydraulic, irrigation, and environmental engineering for diverting and controlling the flow of water in irrigation–drainage systems, drainage canal systems, and wastewater channels. In this study, gene expression programming and group method of data handling were used to estimate the coefficient of discharge for rectangular side weir under subcritical flow condition. Based on dimensional analysis, the coefficient of the discharge depends on the ratio of the crest height to length, ratio of the width of channel to crest length, ratio of the upstream depth in the channel to crest length and the approach Froude number. The performance of the proposed GMDH and GEP model is based on the coefficient of correlation (0.91), mean absolute percentage error (3.54), average absolute deviation (3.3), root mean square error (0.027) and the coefficient of correlation (0.905), mean absolute percentage error (4.12) average absolute deviation (3.9), root mean square error (0.029), respectively. Finally, the results reveal that GMDH model could provide more satisfactorily estimations as compared to those obtained by traditional regression and GEP models.
- Artificial intelligence and machine learning in water resources engineering
- Prediction of Discharge Coefficient of Circular Side Orifice Through Machine Learning Technique A sharp-crested circular side orifice is a crucial element when it comes to diverting flow from primary source to its subordinate source. Such a flow measurement instrument technique is of immense value in conservation and evaluation of drainage and irrigation networks. Usually, it is placed towards the side of a channel in order to regulate the flow of the fluid. Traditionally, coefficient of discharge was predicted through regression methods which are time-consuming and lack accuracy. Artificial Intelligence (AI) and its applications in this domain have bridged this gap by providing novel alternative methods which prove much more efficient. Repeated studies have pointed out that AI techniques generally give better results when it comes to a myriad of water variables such as rainfall-runoff, evaporation and evapotranspiration, streamflow, and dam water level changes. Total 261 dataset has been collected from the literature review comprising of the fully submerged orifice and for partially-submerged orifice with varying orifice diameter (D) of 5 cm, 10 cm and 15 cm. This study aims to provide a better estimate of prediction of discharge through circular sharp-crested orifice using Artificial Neural Network (ANN). The ANN model has been deployed to randomly select 80% of the data for training, 15% for validation and remaining 5% for testing. In the ANN model, Lavenberg-Marquardt algorithm was used as back-propagation step to assign weights in order to predict the output. The correlation coefficient (R), mean absolute error (MAE) and root mean squared error (RMSE) for complete data of fully and partially submerged circular side orifice are observed to be 0.9765, 0.0228 and 0.0172 respectively.
- Application of ANN in Estimating IRED of Stepped Gabion Weir A rectangular basket assembled from a hexagonal mesh of heavily galvanised steel wire, filled with rock stacked atop one another to form a weir structure, is known as a Gabion weir. They are porous structures that can sometimes be vegetated and are considered an aesthetic structural solution with minimal habitat. Recently, the stepped gabion weirs have become a popular structure replacing stepped spillways that can check floods. The performance of an artificial neural network, one of the robust machine learning techniques, is investigated in predicting the inverse relative energy dissipation of the stepped gabion weir. The proposed ANN model in the present study is then compared with different machine learning techniques available in the literature. Based on performance parameters, it is observed that the proposed ANN model has the highest accuracy compared to the GMDH and GEP models in predicting the relative energy dissipation of the stepped gabion weir.
- Dimensions and Units (Lecture-2)
- The Properties of Fluid (Lecture-3)
- The Concept of Fluid (Lecture-1)
- Introduction to gradually varied flows
- Gradually varied flow equations
- Classification of gradually varied flow
- Classification of gradually varied flow-2
- Gradually varied flow profiles with change in bed slopes
- Gradually varied flow computations
- Rapidly varied flowintroduction
- Theoretical aspects of hydraulic jump
- Characteristics of jumps in rectangular channel
- Features of hydraulic jumps
- Jumps as energy dissipators
- Surges-1
- Surges-2
- BCE303 Hydraulicshttps://www.youtube.com/embed/DvHTcpE7qSY
- Dam Engineeringhttps://www.youtube.com/embed/2ulvlkfvcP8
- Dam Engineeringhttps://www.youtube.com/embed/dI6KPppifbs