Janga Reddy Manne
IIT Bombay, Civil Engineering, Faculty Member
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Modelling suspended sediment load (SSL) from rivers is a complex problem in river basin management. This chapter presents hybrid framework multivariate empirical mode decomposition (MEMD) and stepwise linear regression (SLR) for... more
Modelling suspended sediment load (SSL) from rivers is a complex problem in river basin management. This chapter presents hybrid framework multivariate empirical mode decomposition (MEMD) and stepwise linear regression (SLR) for estimation of SSL from riverflows demonstrated to a case study in Mahanadi River Basin, India. The method involves two major steps: first, the multivariate dataset comprising SSL of current time along with lagged inputs of streamflow and SSL are decomposed into different modes using MEMD; then, the obtained modes are estimated independently by SLR fitting engaging the statistically significant inputs at respective time scales. The sum of the predicted modes gives the desired SSL. The effectiveness of the presented method is evaluated for five models by considering different combinations of inputs, and their performance is compared with traditional multiple linear regression (MLR) and model tree (MT) models. The performance statistics of models showed that fo...
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Physically based distributed hydrological models are an invaluable tool for planning and management of water resource projects. However, a reliable prediction from hydrological models can only be expected if their unknown model parameters... more
Physically based distributed hydrological models are an invaluable tool for planning and management of water resource projects. However, a reliable prediction from hydrological models can only be expected if their unknown model parameters are estimated accurately. In place of laborious manual calibration of the parameters of the hydrological model, this study presents an automatic calibration scheme for the VIC-RAPID hydrological model using the self-adaptive differential evolution (SaDE) algorithm. The SaDE eliminates the laborious manual tuning of the two control parameters (mutation factor and crossover rate) of the conventional DE algorithm. The proposed approach is demonstrated with a case study in the upper Krishna river sub-basin for estimation of the 15 VIC-RAPID model parameters. The efficacy of the proposed calibration technique for hourly streamflow simulation is evaluated by using standard performance measures such as Nash-Sutcliffe Coefficient (NSE), Coefficient of Corr...
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This book presents the principles and practices of optimal design of irrigation canals considering varying roughness along channel perimeter incorporating slope stability constraint, hydraulic & geometric restrictions, uncertainty... more
This book presents the principles and practices of optimal design of irrigation canals considering varying roughness along channel perimeter incorporating slope stability constraint, hydraulic & geometric restrictions, uncertainty associated with the roughness parameter etc. Swarm intelligence based method is followed in optimizing different open channel cross sections and the novel methodologies presented in this book have practical appeal in the design of irrigation canals ensuring cost effectiveness and system reliability.
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AbstractThis paper investigates the use of surrogate measures as potential substitutes for reliability in multiobjective design of water distribution networks (WDNs). Assessing WDN reliability with...
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This study investigates the change in dependence structure of bivariate flood flow characteristics namely magnitude, frequency and timing in the Godavari river basin using a copula based likelihood ratio (CLR) test. Parametric bootstrap... more
This study investigates the change in dependence structure of bivariate flood flow characteristics namely magnitude, frequency and timing in the Godavari river basin using a copula based likelihood ratio (CLR) test. Parametric bootstrap was used to obtain critical values of CLR test for the best-fitted copula. The performance of the CLR method was also evaluated for simulated synthetic bivariate series with a known change-point location in the dependence (copula parameter). Then the methodology was applied to two streamflow monitoring sites Bhimkund and Wagholi-Butti, located in the Godavari river basin in India. Streamflow data for 33 years from 1977 to 2009 was analyzed, by extracting the series of flow characteristics of magnitude, frequency and timing. Initially univariate change-point detection (CPD) test namely standard normal homogeneity test was applied to detect abrupt change-point in mean of the flow series. At Bhimkund site, there was abrupt increase in mean of magnitude, frequency and timing series after the identified change-point year. However, at Wagoli-Butti site, there was abrupt decrease in mean of magnitude and frequency series although timing series got delayed (i.e., abrupt increase). After univariate CPD in mean, the bivariate series i.e., magnitude-frequency and magnitude-timing pairs for these sites were analyzed to detect the change-points in dependence in terms of copula parameters using the CLR method. The results showed that change-points in the copula parameters were detected at year 2003 and 2004 for Wagoli- Butti and Bhimkund sites respectively, and appear to be jointly non-stationary due to human induced change at these two sites. The results of study for detection of the change-point location in the dependency structure of flow characteristics would be useful for flood risk assessment in the basin.
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ABSTRACT This study presented multiscale characterization of monthly streamflow time series using Multivariate Empirical Mode Decomposition (MEMD) and developed an innovative approach for streamflow prediction by coupling MEMD with... more
ABSTRACT This study presented multiscale characterization of monthly streamflow time series using Multivariate Empirical Mode Decomposition (MEMD) and developed an innovative approach for streamflow prediction by coupling MEMD with Genetic Programming (GP). Firstly, the possible hydro-climatic teleconnection of monthly streamflows of Mahanadi river basin in India with two large-scale climate oscillations of ElNiño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO) is investigated by applying MEMD based Time-Dependent Intrinsic Correlation (TDIC) analysis. The TDIC analysis showed that the association between large-scale climate oscillations and streamflows is not unique always, but both the nature and strength of the association varies with time scales and over the time domain. Based on this finding, the study proposed MEMD-GP coupled approach for streamflow prediction, in which different modes corresponding to different process scales obtained by the MEMD are predicted separately using GP; and summation of these predicted modes provides the monthly streamflow at the station. A statistical performance evaluation based on multiple criteria showed that the proposed approach performs better than the multiple linear regression, M5 model tree and GP models for monthly streamflow prediction including extreme low and high flows, due to its unique capability to include the significant predictors at different time scales.
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In this study, the performance of a recent meta-heuristic technique, namely, gravitational search algorithm (GSA) is evaluated for a deterministic as well as for a probabilistic design of canals that have cross-sectional shape of... more
In this study, the performance of a recent meta-heuristic technique, namely, gravitational search algorithm (GSA) is evaluated for a deterministic as well as for a probabilistic design of canals that have cross-sectional shape of horizontal bottom and parabolic sides (HBPS). The uncertainty in various input parameters is modeled using probabilistic approach and a modified chance-constrained model is presented for an optimal design of HBPS canals with the aim of minimizing the total cost, while satisfying the basic flow constraints and reliability constraints on the canal capacity and overtopping. First, the GSA method is applied to solve the HBPS canal design problem under different constraints, and its performance is evaluated by comparing with the solutions of the deterministic models by the particle swarm optimization and genetic algorithm. Then, the GSA is applied to obtain the solution of the probabilistic model and in view of multiple conflicting goals; pseudo-weight vector approach is adopted to assist in decision-making and demonstrate its applicability for arriving at a suitable decision. The results obtained suggest that the proposed GSA approach has good potential for a reliable and cost-effective design of canals.
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Research Interests: Environmental Engineering, Civil Engineering, Geography, Nonparametric Statistics, Climatology, and 15 morePrediction, Atmospheric sciences, Variability, United States, Kerala, Streamflow, Temperature Trends, Rainfall, Trend Analysis, Precipitation, Series, Discrete wavelet transform, Northwest, Monsoon Rainfall, and Subdivision
In this paper, a stochastic model is presented to generate monthly inflows into the Hirakud Dam by using the principles of entropy and copulas. The marginal distributions obtained using the principle of maximum entropy (POME) helps the... more
In this paper, a stochastic model is presented to generate monthly inflows into the Hirakud Dam by using the principles of entropy and copulas. The marginal distributions obtained using the principle of maximum entropy (POME) helps the model to retain the higher-order moment statistics of the historical data, whereas the copula-based joint distributions ensure the preservation of the dependence between the adjacent months. On analysing the moments and dependence structures of the generated data samples and comparing with the corresponding observed data properties, it is found that the developed stochastic models gives good performance by preserving flow properties. Hence the entropy-copula-based model can be used as an effective approach for stochastic generation of hydrological variables.
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Identification of homogenous regions and their ranking is important to formulate appropriate strategies for suitable conservation and management practices within the watershed. In this study, Kohonen neural network (KNN) is employed to... more
Identification of homogenous regions and their ranking is important to formulate appropriate strategies for suitable conservation and management practices within the watershed. In this study, Kohonen neural network (KNN) is employed to classify the micro-watersheds of Kaddam watershed in middle Godavari basin (India) into homogeneous groups. KNN algorithm learns to cluster groups of similar input patterns from a high-dimensional input space in a non-linear fashion onto a low-dimensional layer of neurons. Ten geo-morphological parameters are used for classification of micro-watersheds. An optimal number of groups is chosen based on two cluster validation measures, the Davies–Bouldin Index and the Dunn’s Index. By using the KNN method, 18 micro-watersheds are grouped into five homogenous groups based on selected watershed parameters. The clustering results showed concurrence with general ranking of micro-watersheds. Further, the KNN micro-watershed classification results are validated by comparing with the results of K-means clustering algorithm (KCA). From the comparative analysis, it is observed that both the algorithms give approximately similar kinds of classification of micro-watersheds. The obtained results help in identifying the groups of micro-watersheds that should be given top priority (i.e. those that require immediate conservation measures). The study suggests that identifying homogeneous regions can be helpful for effective planning and management of watersheds, and KNN can be applied effectively for micro-watershed zonation.
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ABSTRACT This study investigated the utility of two meta-heuristic algorithms to estimate parameters of copula models and for derivation of drought severity–duration–frequency (S–D–F) curves. Drought is a natural event, which has huge... more
ABSTRACT This study investigated the utility of two meta-heuristic algorithms to estimate parameters of copula models and for derivation of drought severity–duration–frequency (S–D–F) curves. Drought is a natural event, which has huge impact on both the society and the natural environment. Drought events are mainly characterized by their severity, duration and intensity. The study adopts standardized precipitation index for drought characterization, and copula method for multivariate risk analysis of droughts. For accurate estimation of copula model parameters, two meta-heuristic methods namely genetic algorithm and particle swarm optimization are applied. The proposed methodology is applied to a case study in Trans Pecos, an arid region in Texas, USA. First, drought severity and duration are separately modeled by various probability distribution functions and then the best fitted models are selected for copula modeling. For modeling the joint dependence of drought variables, different classes of copulas, namely, extreme value copulas, Plackett and Student’s t copulas are employed and their performance is evaluated using standard performance measures. It is found that for the study region, the Gumbel–Hougaard copula is the best fitted copula model as compared to the others and is used for the development of drought S–D–F curves. Results of the study suggest that the meta-heuristic methods have greater utility in copula-based multivariate risk assessment of droughts.
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<p>This study intended to understand the effect of climate change on spatiotemporal characteristics of multivariate drought risk over the Vidarbha region of India. The Standardized Precipitation Evapotranspiration... more
<p>This study intended to understand the effect of climate change on spatiotemporal characteristics of multivariate drought risk over the Vidarbha region of India. The Standardized Precipitation Evapotranspiration Index (SPEI) is employed to characterize droughts in the region. Gridded daily precipitation and temperature data produced by the Indian Meteorological Department (IMD) and Coupled Model Inter-comparison Project Phase 6 (CMIP6) were utilized for estimating the SPEI. The drought events were identified and subsequently characterized by duration, severity, and peak. Different goodness of fit tests was applied to select the best fitting marginal distributions of the individual drought characteristics. Several symmetric and asymmetric Archimedean trivariate copulas and seven bivariate copula families were evaluated for joint distribution modeling. Maximum pseudo-likelihood and genetic algorithms have been applied to estimate the copula parameters accurately. The asymmetric Frank copula was selected to construct the trivariate distribution of the drought characteristics. Frank, Student’s <em>t</em> and Clayton copulas were chosen to build the bivariate distribution of duration-severity, duration-peak, and severity-peak, respectively. The joint distributions were applied for computing the joint return periods of drought events. The drought risk over the region was illustrated using zoning maps for historical along with near and far future periods. The inferences derived from the study will help policymakers to prepare better mitigation strategies under the changing environment.</p>
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During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for... more
During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources...
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This paper presents a derivation of optimal operation policies for hydropower production in the Upper Seti Hydro-Power Reservoir system in Nepal using particle swarm optimization (PSO) technique. A reservoir operation model for the Upper... more
This paper presents a derivation of optimal operation policies for hydropower production in the Upper Seti Hydro-Power Reservoir system in Nepal using particle swarm optimization (PSO) technique. A reservoir operation model for the Upper Seti project is formulated with an objective of maximising the annual hydropower production operated at a weekly time scale subjected to various physical and operational constraints. An elitist-mutated PSO (EMPSO) technique is applied for solving the weekly reservoir operational model, and the EMPSO-based solutions are found to result in 3% more hydropower than the planned hydropower production. The reservoir operation policies are also compared for wet, dry and normal water years, and it is noted that there exist significant differences among the release policies for those hydrologic conditions. Later, the reservoir operation model is modified with an objective of minimising the annual sum of squared deviation between weekly energy production and target hydropower. Then the hydropower analysis is carried out for various target hydropower values with an aim of finding suitable firm-power for the project. The performances of various reservoir operation policies are evaluated using reliability, resilience and vulnerability measures. The sustainability of the system is evaluated by computing the sustainability index, which is then used to evolve suitable hydropower targets. It is found that a target hydropower of 4.8 GWh with a sustainability index of 0.75 may result in better overall performance of the system.
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Efficient design and operation of water resource systems is a challenging task in many real world applications. Many issues related to water resources require the solutions of optimization. As computers have become more powerful, the size... more
Efficient design and operation of water resource systems is a challenging task in many real world applications. Many issues related to water resources require the solutions of optimization. As computers have become more powerful, the size and complexity of problems which can be simulated and solved by optimization techniques have correspondingly expanded. Real life water management problems involve nonlinear optimization and often associated with complexities of non-convex objective functions and multimodal solutions. If the objective function is not known analytically, traditional methods are not applicable. Consequently these difficulties lead to go for non-conventional optimization techniques. Recently evolutionary computation techniques have been receiving increased attention in view of their potential as global optimization techniques for complex problems. This popularity is mainly due to the robustness, ease of use and wide applicability of evolutionary algorithms. This paper ...
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Extreme hydrological events such as droughts can cause substantial damage to society and ecosystem. Droughts can be characterized by severity, duration, spatial extent and frequency of occurrences. In this study gridded (0.5o latitude ×... more
Extreme hydrological events such as droughts can cause substantial damage to society and ecosystem. Droughts can be characterized by severity, duration, spatial extent and frequency of occurrences. In this study gridded (0.5o latitude × 0.5o longitude) daily precipitation data for the year 1971-2005 from Western Rajasthan meteorological subdivision of India are used to develop monthly time series of Standardized Precipitation Index (SPI). The drought conditions are identified based on SPI percentile value of 20% or below for each grid point in the study domain. The spatial identification of drought is based on spatial contiguity of SPI values aggregated at a time scale of six months (SPI 6) at each pixel. Drought severity is assessed from spatial mean SPI value of drought cluster identified at successive monthly time scale. It is found that drought severity and spatial extent is negatively correlated with each other. The drought severity is best described by generalized extreme value distribution and spatial extent using log normal distribution. The joint distribution of drought severity and spatial extent are modeled using bivariate Plackett and Archimedean class of Frank copulas. Standard goodness-of-fit test suggests that Plackett copula as a more suitable model. The copula based joint distribution of drought severity and spatial extent is employed to derive conditional return period, which in turn is used to derive drought severity-area-frequency (SAF) curves. The results of the study can be useful in water resources planning in drought affected areas and for deciding drought management policies.
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Abstract This study presents risk assessment of hydrologic extreme events droughts in Saurashtra and Kutch region of Gujarat state, India. Drought is a recurrent phenomenon and risk assessment of droughts can play an important role in... more
Abstract This study presents risk assessment of hydrologic extreme events droughts in Saurashtra and Kutch region of Gujarat state, India. Drought is a recurrent phenomenon and risk assessment of droughts can play an important role in proper planning and management of water resources in the study region. In the study, drought events are characterized by severity and duration, and drought occurrences are modeled by Standardized Precipitation Index (SPI) computed on mean areal precipitation, aggregated at a time scale of 6 months ...
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Abstract In this paper, a copula based methodology is presented for flood frequency analysis of Upper Godavari River flows in India. By using the specific advantages of copula method in modeling the joint dependence structure of uncertain... more
Abstract In this paper, a copula based methodology is presented for flood frequency analysis of Upper Godavari River flows in India. By using the specific advantages of copula method in modeling the joint dependence structure of uncertain variables, this study applies Archimedean copulas for frequency analysis of flood characteristics annual peak flow, flood volume and flood duration. To determine the best fit marginal distributions for flood variables, few parametric and nonparametric probability distributions are examined and ...
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ABSTRACT This paper presents optimization and uncertainty analysis of operation policies for Hirakud reservoir system in Orissa state, India. The Hirakud reservoir project serves multiple purposes such as flood control, irrigation and... more
ABSTRACT This paper presents optimization and uncertainty analysis of operation policies for Hirakud reservoir system in Orissa state, India. The Hirakud reservoir project serves multiple purposes such as flood control, irrigation and power generation in that order of priority. A 10-daily reservoir operation model is formulated to maximize annual hydropower production subjected to satisfying flood control restrictions, irrigation requirements, and various other physical and technical constraints. The reservoir operational model is solved by using elitist-mutated particle swarm optimization (EMPSO) method, and the uncertainty in release decisions and end-storages are analyzed. On comparing the annual hydropower production obtained by EMPSO method with historical annual hydropower, it is found that there is a greater chance of improving the system performance by optimally operating the reservoir system. The analysis also reveals that the inflow into reservoir is highly uncertain variable, which significantly influences the operational decisions for reservoir system. Hence, in order to account uncertainty in inflow, the reservoir operation model is solved for different exceedance probabilities of inflows. The uncertainty in inflows is represented through probability distributions such as normal, lognormal, exponential and generalized extreme value distributions; and the best fit model is selected to obtain inflows for different exceedance probabilities. Then the reservoir operation model is solved using EMPSO method to arrive at suitable operational policies corresponding to various inflow scenarios. The results show that the amount of annual hydropower generated decreases as the value of inflow exceedance probability increases. The obtained operational polices provides confidence in release decisions, therefore these could be useful for reservoir operation.
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ABSTRACT This study presents spatio-temporal analysis of droughts in one of the most drought prone region in India–western Rajasthan and develops drought intensity-area-frequency curves for the region. The meteorological drought... more
ABSTRACT This study presents spatio-temporal analysis of droughts in one of the most drought prone region in India–western Rajasthan and develops drought intensity-area-frequency curves for the region. The meteorological drought conditions are analyzed using 6-month standardized precipitation index (SPI-6) estimated at spatial resolution of 0.5° × 0.5°. Spatio-temporal analysis of SPI-6 indicates increase in frequency of droughts at the central part of the region. The non-parametric Mann–Kendall test for seasonal trend analysis showed increase in number of grids under drought during the study period. Further, bivariate frequency analysis of drought characteristics—intensity and areal extent is carried out using copula methods. For modeling joint dependence between drought variables, three copula families namely Gumbel-Hougaard, Frank and Plackett copulas are evaluated. Based on goodness-of-fit as well as upper tail dependence tests, it is found that the Gumbel-Hougaard copula best represents the drought properties. The copula-based joint distribution is used to compute conditional return periods and drought intensity–area–frequency (I–A–F) curves. The I–A–F curves could be helpful in risk evaluation of droughts in the region.
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ABSTRACT In this paper, a bivariate-copula-based methodology is presented to assess the risk associated with hydroclimatic variability on groundwater levels in an unconfined aquifer at the Manjara basin in India. Rank correlation analysis... more
ABSTRACT In this paper, a bivariate-copula-based methodology is presented to assess the risk associated with hydroclimatic variability on groundwater levels in an unconfined aquifer at the Manjara basin in India. Rank correlation analysis is used to identify the association between the El Nino-Southern Oscillation (ENSO) index, precipitation, and groundwater levels. It is found that the dependencies among the hydroclimatic variable pairs are statistically significant and the dependence structure can be modeled by using bivariate Archimedean copulas. The groundwater level or depth-to-groundwater table (DGWT) in the study region is found to be responsive toward interannual precipitation variations that are influenced by the ENSO phenomenon. For probabilistic representation of hydroclimate variables, various probability distributions are evaluated and it is found that the precipitation and DGWT are best fitted using lognormal and Weibull distributions, respectively, whereas the ENSO index is best fitted using nonparametric-based normal kernel density function. For modeling joint dependence structure of hydroclimatic variable pairs (precipitation-DGWT, ENSO index-precipitation, and ENSO index-DGWT), appropriate Archimedean copulas (viz, Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank families) are evaluated. On performing standard statistical tests, it is found that the Frank copula is best representing the joint dependence structure for all three variable pairs. Then the Frank copula-based joint distributions are used to derive conditional distributions and to perform risk analysis of groundwater levels. The study suggest that the copula-based methodology can be used effectively for modeling dependence structure of hydroclimatic variables and for risk assessment of groundwater levels under changes in hydroclimatic conditions. DOI: 10.1061/(ASCE)HE.1943-5584.0000564. (C) 2012 American Society of Civil Engineers.
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Research Interests: Environmental Engineering, Civil Engineering, Environmental Science, Decision Making, Particle Swarm Optimization, and 15 moreReservoir Operation, Soil moisture, Case Study, Evapotranspiration, Global Optimization, Water Allocation, Soil Heterogeneity, Water Resource, Water Deficit, Crop Yield, Economic Benefit, Particle Swarm Optimizer, Objective function, Model integration, and Root growth
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Abstract: This paper presents cross entropy (CE) based methodology for optimal design of water distribution network (WDN). Design of WDN involves selection of suitable diameter for each pipe in the network from the list of commercially... more
Abstract: This paper presents cross entropy (CE) based methodology for optimal design of water distribution network (WDN). Design of WDN involves selection of suitable diameter for each pipe in the network from the list of commercially available diameters. The CE methodology is applied to two bench mark WDN design problems taken from literature for validation. The first WDN problem deals with determining optimal pipe sizes for planning a new system, while the second WDN deals with rehabilitation of existing WDN by parallel ...
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Traditional optimization methods are no longer adequate to solve complex real life problems, as most of them involve nonlinear, discontinuous, non- differentiable, nonconvex, multiobjective functions with mixed variables in their model... more
Traditional optimization methods are no longer adequate to solve complex real life problems, as most of them involve nonlinear, discontinuous, non- differentiable, nonconvex, multiobjective functions with mixed variables in their model formulation. Over the last few years, the use of nature inspired meta-heuristic algorithms for systems optimization has increased tremendously, and “swarm intelligence and evolutionary computing techniques” are rapidly emerging as powerful tools for solving practical problems. This book describes efficient computational techniques based on Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) principles for single and multiple criterion optimization; and hybrid soft-computing techniques such as PSO Neural Network (PSO-NN), Adaptive Network Fuzzy Inference System (ANFIS) for hydrologic forecasting and demonstrates their applications to case studies in reservoir systems operation. This b...