S. Adarsh

Overtopping Probability Constrained Optimal Design of Composite Channels Using Swarm Intelligence Technique

In this paper swarm intelligence based methodology is proposed for optimal and reliable design of irrigation channels. The input parameters involved in channel design are prone to uncertainty and the solution of deterministic model may result in flooding risk and affect the stability of the channel. To provide reliability in the design, an overtopping probability constrained design is presented in this study. The deterministic equivalent of the probabilistic constraint is derived by following the principle of first order uncertainty analysis. In order to account for the uncertainty of design parameters in the objective function, a modified cost function is proposed. A methodology is propounded to solve it in a metaheuristic environment and solved it using elitist-mutated particle swarm optimization (EMPSO) method. The EMPSO based solutions are found to be quite successful and better than the classical optimization methods. Finally, it is concluded that the proposed methodology has a good potential for reliable design of composite channels for designer specified reliability values.

Multiscale characterization of streamflow and suspended sediment concentration data using Hilbert–Huang transform and time dependent intrinsic correlation analysis

In this paper, Hilbert–Huang transform method is applied for the characteristic analysis of monthly streamflow and total suspended sediment (TSS) concentration time series from two stations, Basantpur and Tikrapara in Mahanadi basin, India. All the four time series are first decomposed by the complete ensemble empirical mode decomposition with adaptive noise method into different Intrinsic Mode Functions (IMFs) with specific periodicity and the IMFs are subsequently subjected to the Hilbert transform. The multiscale decomposition clearly detected the annual cycle in all the four time series and statistical significance test of the IMF components showed that the annual and inter annual cycles are significant in deciding the variability of the series except for the streamflow of Tikrapara station. The cross correlation analysis found a direct association between streamflow and TSS in most of the time scales except in the residue. Finally, the time dependent intrinsic correlation (TDIC) analysis is employed to find the association between streamflow and TSS in different time scales. The TDIC analysis proved the existence of long range direct correlation between the two series at intra-annual and annual time scales for the data from both of the stations. From TDIC analysis, it is further noticed that the nature of association between streamflow and sediment concentration is not of unique character always but varies with time scales and in time domain and many reversals of associations are observable at inter annual scales. The negative association between streamflow and sediment concentration is more perceptible in the data from Tikrapara station than that from Basantpur and it infers the influence of internal forcing such as human interventions and basin characteristics.

Use of Particle Swarm Optimization for Optimal Design of Composite Channels

To surmount the challenges faced by conventional methods such as the requirement of several approximations or simplifications or derivative information on functions of the model, this paper presents the Particle Swarm Optimization (PSO) method for optimal design of open channels. Two variants of channel design models, such as the lumped approach and distributed velocity approach, are formulated with varying degrees of complexities. The PSO method is first applied to solve three site specific models developed based on the lumped approach. The first two models consider geometric constraints and hydraulic constraints separately, and the third model considers all the constraints simultaneously with the basic model. Then a distributed velocity model is formulated considering geometric and hydraulic constraints and solved using PSO method. We found that the solutions of distributed model resulting in lower optimal cost than lumped approach; also the distributed approach gives more realistic channel sections to apply them to field. To facilitate field applicability, optimal design graphs are developed. The results suggest that the PSO method has capability in handling the non-linear, non-convex, and multi-modality nature of different channel design problems and can be used for comprehensive design of open channels.

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time-dependent intrinsic correlation analysis

In this paper, the Hilbert–Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time–frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989–2012 including the four extreme events that have occurred during this period.

Investigating the multiscale variability and teleconnections of extreme temperature over Southern India using the Hilbert–Huang transform

This study investigates the variability of annual minimum and maximum surface temperature (Tmin and Tmax) of three temperature homogeneous regions [East Coast (EC), Western Coast (WC) and Interior Peninsular (IP)] in southern India in multiple time scales and examines their teleconnections with different climatic indicators. First, the different temperature time series are decomposed into appropriate number of oscillatory modes using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. Subsequently, the modes are subjected to spectral analysis employing the Normalized Hilbert Transform—Direct Quadrature (NHT–DQ) scheme to get instantaneous amplitudes and frequencies. Further, a detailed trend analysis of instantaneous amplitudes is performed, which showed that the recent changes in temperature since 1970s, over southern India are mainly attributed to the increase in amplitudes of the IMFs corresponding to inter-decadal periodicity in all the regions. Moreover, data of four climatic indicators such as Pacific Decadal Oscillation (PDO), Sunspot Number (SN), Total Solar Irradiance (TSI) and CO2 concentration are decomposed using CEEMDAN and compared with decompositions of Tmax and Tmin time series of all the three regions. From the cross correlation analysis of oscillatory modes, this study established the links of different climatic indicators with the extreme temperature of southern India, which are evident mainly at few low frequency modes and trend component. The close matching of periodicity of different lower modes of PDO series with that of maximum and minimum temperature of the different regions depicted the possible association of PDO with the temperature regime of southern India. This association is further investigated with a recently developed running correlation analysis method namely Time Dependent Intrinsic Correlation (TDIC), which deciphered a long range positive correlation between the PDO and minimum temperature series at inter annual mode of 7 years to inter decadal mode of 15 years at all the three regions of southern India. The association between PDO and maximum temperature at inter annual ranges is positive at EC and IP regions. The association between PDO and maximum temperature at inter decadal scale show regional differences, with no association at WC region, negative association at IP region and positive association at EC region.

Links Between Global Climate Teleconnections and Indian Monsoon Rainfall

The knowledge of teleconnections elucidates the mechanisms behind the local meteorological processes that are influenced by the ocean/atmospheric circulations, which operate at global scale. As the local meteorological variables such as regional temperature/rainfall and global climate signals possess multiscaling properties, it is highly desired to examine the teleconnections in multiple process scales rather than following conventional statistical correlation analysis and/or based on periodicity estimation. In this context, this chapter presents an investigation of hydroclimatic teleconnections of All India Summer Monsoon Rainfall (AISMR) with large-scale climate oscillations in a multiscaling framework by employing Hilbert–Huang Transform (HHT)-based Time-Dependent Intrinsic Correlation (TDIC) analysis. The study investigated the teleconnections of AISMR for the period 1950–2012, with the four global climate oscillations such as Quasi-Biennial Oscillation (QBO), El Nino Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), and Atlantic Multidecadal Oscillation (AMO) in different process scales. The study found that both the strength and nature of association between global climate oscillations and ISMR vary with process scale and there could be multiple switchovers in the character of such associations over the time domain.

Developing hourly intensity duration frequency curves for urban areas in India using multivariate empirical mode decomposition and scaling theory

Development of rainfall intensity–duration–frequency (IDF) curves of short duration such as hourly or sub-daily are quite essential for planning and design of urban storm water drains. However, in developing countries like India, at many places the meteorological observatories do not have long records of hourly rainfall data, at large which may have access to data at daily or higher time scale only. Therefore, the scale invariance property of rainfall and/or the disaggregation process can be a useful mean for obtaining of shorter duration rainfall IDF curves from the daily scale rainfall data. This study proposes an alternative approach for deriving the hourly and sub-daily IDF relationships by using the scale invariance property of rainfall, empirical mode decomposition (EMD) method and extreme value (EV) model formulations. The multivariate EMD method is used for decomposing the rainfall intensity series of different durations simultaneously into a number of orthogonal modes. The logarithmic plot between probability weighted moments of the orthogonal modes and the duration gives the scaling exponent, which is further used for deriving IDF relationships based on EV formulations. To validate the correctness of the proposed method, first the method is applied for rainfall data of Mumbai and Bangalore cities in India, and the results are compared with that obtained by the classical frequency factor method. The results of IDF relationships derived by the two methods displayed good agreement in general, but noticed a larger deviation for the curves of higher return periods. Then the method is applied for the derivation of IDF relationships for hourly durations from the daily rainfall data of eight major cities in Kerala State in India. The results of the study demonstrated the effectiveness of the proposed approach for data-scarce regions in deriving the short duration IDF relationships from the daily rainfall data.

Gravitational search algorithm for probabilistic design of HBPS canals

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.