Department of Mathematics
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1920
Browse
66 results
Search Results
Item Short-term wind speed prediction with adaptive signal processing based hybrid statistical models(Springer, 2025-03) Pasari, SumantaThe inherent nonlinearity, intermittency, and chaotic nature of wind speed make accurate forecasting challenging. Traditional approaches like standalone time series models and frequency domain analysis struggle to capture these complex characteristics effectively. In light of this, the present study utilizes three self-adaptive signal processing methods, namely empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD) and combines with ARIMA or window-sliding ARIMA (WSARIMA) to develop six hybrid models, namely EMD–ARIMA, EEMD–ARIMA, VMD–ARIMA, EMD–WSARIMA, EEMD–WSARIMA, and VMD–WSARIMA. To illustrate the efficacy of the proposed hybrid models in daily wind speed prediction, four study sites from India with different climates are considered. Based on the analysis of 7 years (08-2015–03-2023) of wind speed data, it is found that: (i) the extracted components of VMD overcome the limitations of EMD and EEMD methods; (ii) the combination of VMD and WSARIMA outperforms any other comparative model, such as ARIMA, WSARIMA, EMD–ARIMA, EEMD–ARIMA, VMD–ARIMA, EMD–WSARIMA, or EEMD–WSARIMA; the VMD–WSARIMA model reduces RMSE by 70–80% compared to the conventional ARIMA model; (iii) finally, as a part of post-processing, the residual analysis of the best fit VMD–WSARIMA model shows desirable characteristics. Therefore, the present study strongly recommends to consider adaptive decomposition based hybrid models in wind speed forecasting at shorter time horizon.Item Earthquake cycle progression in major city regions of Taiwan through nowcasting technique(Springer, 2025-05) Pasari, SumantaThe complex tectonic framework of Taiwan makes it susceptible to devastating earthquakes that originate on both mapped faults, and at times, on unmapped faults. The unmapped faults especially highlight the limitation of conventional fault–based hazard assessment methods, emphasizing the need for alternative approaches. In this context, we implement a surrogate area–based earthquake nowcasting technique to assess the seismic cycle progression in 10 densely populated cities across Taiwan. We utilize the notion of natural times, the inter–event counts of small earthquakes between successive large events, to calculate the Earthquake Potential Score (EPS) for each city region. To derive natural time statistics, we analyze eight reference probability models, including exponential distribution and its variants, exponentiated group of distributions, and heavy–tailed distributions. Statistical inference of 114 observed natural times shows that the exponentiated exponential distribution provides the best fit. As of April 24, 2025, the EPS values (%) for M 6.0 earthquakes in the 10 cities range from 53% to 69%, with the following values: Taipei (69%), Hsinchu (68%), Keelung (67%), Hualien (59%), Nantou (58%), Taitung (57%), Chiayi (56%), Pingtung (55%), Tainan (54%), and Kaohsiung (53%). These EPS values indicate the progression in current earthquake cycle toward a M 6.0 earthquake in the corresponding city region. Moreover, there is a consistency in the nowcast scores despite some variations in threshold magnitudes and city regions. The studied approach and results therein offer valuable insights to decision makers to enhance earthquake preparedness and risk management across Taiwan.Item Earthquake prediction using long short term memory on spatio-temporally segmented data(IEEE, 2023) Pasari, SumantaThis paper describes a machine learning model for predicting earthquakes on the basis of past earthquake data. In particular, this study uses the Long Short Term Memory (LSTM) model, a neural network model designed to operate on time-series data with long-term dependencies. Here, the instrumental earthquake data is considered from three selected locations in Indonesia. First, the dataset is pre-processed by segmenting it into time intervals and space grids. The multi-dimensional time-series data is then fed into the network to output the probability of an earthquake in the next interval. This method was originally introduced by Wang et al. [1] and achieved an accuracy close to 85% on a dataset from Mainland China (1966–2016). To the best of our knowledge, no subsequent works have attempted to reproduce their results on different datasets, or introduce enhancements. This research work has implemented the same model on three different datasets. Further, the softmax activation function is replaced with the sigmoid activation function. This ensures that the probability values of earthquakes occurring in the segmented grids are independent of each other and are not rendered mutually exhaustive or exclusive events. Finally, a failure mode of this model is mentioned by showing that it performs poorly to predict large earthquakes.Item Application of Spiking Neural Networks in Renewable Energy Forecasting(Springer, 2024-12) Pasari, SumantaConsidering the high consumption rates of the non-renewable energy sources as well as their adverse climatic impacts, renewable energy has become a widespread topic of discussion. Among the available renewable resources, solar and wind are the highest contributors. However, the high influence of atmospheric parameters and higher cost involved in energy production prevent the widespread use of renewable energy among common public. The location identification for optimum energy production is also a crucial step for setting up future energy plants. In this regard, here we propose a novel strategy to compare prediction results in terms of loss made by traditional convolutional neural network (CNN) with that of spiking neural network (SNN). Though the SNNs are popularly used for vision related tasks, here we evaluate their efficacy in analyzing time series data of solar irradiance and wind speed. In summary, we provide a comprehensive discussion on SNN and their significance on energy forecasting.Item A Novel Framework for Building Vulnerability Assessment for the 2015 Nepal Earthquake(IEEE, 2023) Pasari, SumantaOn April 25, 2015, a devastating earthquake of magnitude Mw 7.8 hit Nepal, killing around 9000 people and injuring 22000 more. Following the disaster, extensive field research and inspections were conducted in Nepal to determine the extent of damage to the earthquake-affected structures. The post-earthquake investigation procedure becomes extremely difficult due to the vast number of structures and types of buildings in the area. However, knowing a building’s description beforehand can assist in determining the extent of possible damages due to a large event. In light of this, the present study aims to provide an effective formulation for building vulnerability assessment using several parameters, such as number of floors, construction materials, house type (public or private), and age of building. A huge dataset comprising building information of around 3,50,000 buildings on 39 variables is used for this purpose. Six machine learning methods, namely logistic regression, decision-tree classifier, k-nearest neighbor, linear discriminant analysis, random forest, and extreme gradient boosting algorithms are implemented. Based on the score, the grading boosting algorithm is found to be the most suitable algorithm. The findings are helpful for better urban planning, social policymaking, suitable material identification for building construction, and moreover, to set up a national level disaster risk reduction (DRR) strategy to minimize earthquake losses in NepalItem Interseismic slip rate and fault geometry along the northwest Himalaya(OUP, 2023-10) Pasari, SumantaGeodetic networks enable us to investigate interseismic crustal deformation along the northwest Himalaya. Using 144 GNSS surface velocities and a Bayesian inversion model, we estimate the slip rate and fault geometry of the Main Himalayan Thrust (MHT) along six arc-normal transects in the northwest Himalaya. We consider that the fault plane consists of three sections along the décollement, namely the locking zone (0−12 km), the transition zone (10−22 km) and the creeping zone (≥22 km). The MHT is found to be completely locked from the surface down to an average depth of 6 ± 2 km. The locking-to-creeping transition zone along the décollement extends from the edge of the fully locked area to a deeper depth (14 ± 3 km) to the tip of the creeping zone of the MHT (17 ± 2 km) with a slip rate of 1.6 ± 0.9 to 3.7 ± 1.1 mm yr−1. Considering the range of uncertainties between 1−2 mm yr−1 for the GNSS velocities, the inverted slip rate along the transition zone of MHT turns out to be insignificant. Thus, the locking zone along the northwest Himalaya extends from the MFT to ∼111 ± 6 km in the north with a locking depth of ∼17 ± 2 km. The deeper part of the MHT is inferred to be creeping with an average slip rate of ∼19.1 ± 1.9 mm yr−1 along the northwest Himalaya. In addition, we have also illustrated a splay-fault model to account for the fault kinematics along the splay faults and the main décollement. The splay-fault model indicates a distributed slip rate at the locking-to-creeping transition zone and about ∼15 per cent smaller slip rate of the MHT than that of the single-fault model. Further, the checkerboard test and the uniform slip model exhibit the reliability of the current GNSS network and the inversion model (single- and splay-fault models). Overall, the updated fault kinematics inevitably contribute to the improvement of seismic hazard evaluation along the northwest HimalayaItem Cluster analysis of rainfall patterns in Mamminasata: Validation of climate hazards group infrared precipitation with station dataset using observational comparisons(2024) Pasari, SumantaThis study aims to apply the cluster method to objectively classify rainfall patterns in the Mamminasata monsoon region based on observational data and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. Validation was carried out using dichotomous and numerical comparisons with in situ observational data to strengthen the validity of CHIRPS data. The results showed that observational and CHIRPS data showed three different rainfall patterns. The first pattern shows rainfall at the beginning or end of the year ranging from 80-110 mm, decreasing after the 9th day of March, then increasing again in May to mid-June. The second pattern has a slightly lower rainfall intensity than the first one. The third pattern shows rainfall that increases beyond 100 mm at the end of each year, decreasing after February and reaching its lowest point in August. Towards the end of the year, there is an increase exponentially. CHIRPS rainfall predictions tend to overestimate in the southwest coastal area of Mamminasata, while underestimations are observed in the northern urban areas and eastern mountains. The accuracy of satellite rainfall estimates varies significantly across the Mamminasata region. In general, the performance of CHIRPS rainfall estimates is better in lowland areas than in mountainous areas.Item Kinematics of crustal deformation along the central Himalaya(Springer, 2024) Pasari, SumantaUtilizing an updated dataset of 145 GNSS surface velocities, this study examines the fault slip rate and fault geometry along the Main Himalayan Thrust (MHT) in the central Himalaya. Employing a Bayesian inversion model, the present analysis reveals that the upper portion of the MHT ramp exhibits full locking, while the lower flat displays creeping motion. The estimated locking depth and fault depth of MFT range from 4.3 ± 2.6 km to 9.7 ± 2.2 km and 13.5 ± 3.1 km to 15.8 ± 1.9 km, respectively, along the central Himalaya. Further, the slip rate along the transition zone lies in the range of 1.4 ± 0.8 mm/yr to 2.7 ± 0.5 mm/yr. Considering the amount of uncertainties as ~1–2 mm/yr in GNSS velocities, the study suggests that the transition zone along the middle flat of the MHT also exhibits locking behavior. Thus, the estimated locking depth extends to ~15.0 km down-dip and covers a horizontal distance of ~90 km (locking line) on the surface, reaching the foothills of the Higher Himalaya. Furthermore, along the deeper flat of the MHT, the slip rate ranges from 19.4 ± 2.5 mm/yr in the west to 12.8 ± 1.6 mm/yr in the east along Nepal Himalaya. The analysis also calculates the slip deficit rate along the MHT fault plane, revealing values of ~15.1 mm/yr in western Nepal, ~12.7 mm/yr in central Nepal, and ~10.6 mm/yr in eastern Nepal. These slip deficit rates across different segments of central Nepal indicate the potential for large earthquakes in the region. The results are further supported by a resolution test using a checkerboard synthetic model, demonstrating the capability of the GNSS network to capture the slip rate along the MHT. These findings inevitably contribute to a comprehensive assessment of the seismic hazard potential in the central Himalayan region.Item High-resolution velocity and strain rate fields in the Kumaun Himalaya: An implication for seismic moment budget(Elsevier, 2024-06) Pasari, SumantaThe collision between Indian and Eurasian tectonic plates results in a series of earthquakes, releasing stored elastic strain accumulated over a long period. This research utilizes 22 new and 26 previously published GPS velocities along with nine years of InSAR observations to estimate high-resolution velocity and strain rate fields across the Kumaun Himalaya. The resulting high-resolution velocity field ranges between 0.5 and 14 mm/yr relative to the India-fixed reference frame. The geodetic strain rate is not uniform across the study region and the higher strain rates are observed along the Main Central Thrust. The areal change rate along the Kumaun Himalaya indicates a significant amount of tectonic compression, with an average value of − 0.08 μstrain∕yr, while the maximum shear strain rate in the region has a mean value of 0.08 μstrain∕yr. The moment deficit rate, based on accumulated strain and energy release over 200 years, turns out to be 7.59 × 1018Nm∕yr along the Kumaun Himalaya. This suggests that the study region can generate a great earthquake (Mw 8.1) in the future.Item Recurrence statistics of M ≥ 6 earthquakes in the Nepal Himalaya: formulation and relevance to future earthquake hazards(Springer, 2024-03) Pasari, SumantaRecurrence statistics of large earthquakes has a long-term economic and societal importance. This study investigates the temporal distribution of large (M ≥ 6) earthquakes in the Nepal Himalaya. We compile earthquake data of more than 200 years (1800–2022) and calculate interevent times of successive main shocks. We then derive recurrence-time statistics of large earthquakes using a set of twelve reference statistical distributions. These distributions include the time-independent exponential and time-dependent gamma, lognormal, Weibull, Levy, Maxwell, Pareto, Rayleigh, inverse Gaussian, inverse Weibull, exponentiated exponential and exponentiated Rayleigh. Based on a sample of 38 interoccurrence times, we estimate model parameters via the maximum likelihood estimation and provide their respective confidence bounds through Fisher information and Cramer–Rao bound. Using three model selection approaches, namely the Akaike information criterion (AIC), Kolmogorov–Smirnov goodness-of-fit test and the Chi-square test, we rank the performance of the applied distributions. Our analysis reveals that (i) the best fit comes from the exponentiated Rayleigh (rank 1), exponentiated exponential (rank 2), Weibull (rank 3), exponential (rank 4) and the gamma distribution (rank 5), (ii) an intermediate fit comes from the lognormal (rank 6) and the inverse Weibull distribution (rank 7), whereas (iii) the distributions, namely Maxwell (rank 8), Rayleigh (rank 9), Pareto (rank 10), Levy (rank 11) and inverse Gaussian (rank 12), show poor fit to the observed interevent times. Using the best performed exponentiated Rayleigh model, we observe that the estimated cumulative and conditional occurrence of a M ≥ 6 event in the Nepal Himalaya reach 0.90–0.95 by 2028–2031 and 2034–2037, respectively. We finally present a number of conditional probability curves (hazard function curves) to examine future earthquake hazard in the study region. Overall, the findings provide an important basis for a variety of practical applications, including infrastructure planning, disaster insurance and probabilistic seismic hazard analysis in the Nepal Himalaya.