Geomorphology-Wavelet based approach to rainfall runoff modeling for data scarce semi-arid regions, Kolar River catchment, India

The conceptual and physical mathematical model of rainfall-runoff modeling uses various parameters such as land use land cover, soil type classification, rainfall, atmospheric data such as temperature, evapotranspiration, solar radiation and wind speed, etc. But these data may not be available for developing countries and data scares semi-arid watershed. Also, the problem is even more critical for ungauged catchments and where manual record is maintained of water level and rainfall data. To address this issue, trend analysis is performed using Mann-Kendall test and Sen’s slope test which shows significant trend change stressing the need for new method for runoff prediction for better water resource management. In this study, a total of four models namely nonlinear autoregressive model with exogenous inputs lumped (LNARX), nonlinear autoregressive model with exogenous geomorphometrically processed inputs (GNARX), wavelet nonlinear autoregressive model with exogenous inputs (WLNARX) and nonlinear autoregressive model with exogenous geomorphometrically processed inputs (WGNARX). Ten models with different input combinations were selected based on their performance are analyzed for all the four networks. The best performing model for these networks is model no. 6 with WGNARX network with NSE 0.97 and RMSE 0.97 and with least value of RMSE. This method can be applied to data scarce region where data available are available for shorter duration and helpful for ungauged catchments also.

Forecasting CO2 Emissions Due To Electricity Power Generation In Bahrain

Global warming is one of the biggest challenges among the leaders and scientists from developed and developing countries. Rapid industrialization and urbanization have given the boost to the amount of greenhouse gases’ emission. Carbon dioxide (CO2) is a significant of greenhouse gases and is the major contributing factor for global warming. CO2 emissions concentrations in the atmosphere have increased by 47% over the past 170 years due to human activities. As per Doha amendment of the Kyoto protocol in 2012, the target for maximum CO2 emission per capita for Bahrain was set to 20.96 metric ton for 2020. However, the current amount of CO2 emission per capita is 21.64 metric ton as of 2019. This research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression and Holt’s methods for CO2 emission forecasting. It attempts to forecast the CO2 emission of Bahrain. These methods are evaluated for performance. Neural network model has the RMSE of merely 0.206 while the GPR-RQ model has RMSE of 1.0171 and Holt’s method has RMSE of 1.4096. Therefore, it can be concluded that neural network time series nonlinear autoregressive model has performed better for forecasting the CO2 emission of Bahrain.

Memory-Based Reduced Modelling and Data-Based Estimation of Opinion Spreading

We investigate opinion dynamics based on an agent-based model and are interested in predicting the evolution of the percentages of the entire agent population that share an opinion. Since these opinion percentages can be seen as an aggregated observation of the full system state, the individual opinions of each agent, we view this in the framework of the Mori–Zwanzig projection formalism. More specifically, we show how to estimate a nonlinear autoregressive model (NAR) with memory from data given by a time series of opinion percentages, and discuss its prediction capacities for various specific topologies of the agent interaction network. We demonstrate that the inclusion of memory terms significantly improves the prediction quality on examples with different network topologies.

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

Compound Autoregressive Network for Prediction of Multivariate Time Series

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.

On stability Conditions of Burr X Autoregressive model

This article deals with proposed nonlinear autoregressive model based on Burr X cumulative distribution function known as Burr X AR (p), we demonstrate stability conditions of the proposed model in terms of its parameters by using dynamical approach known as local linearization method to find stability conditions of a nonzero fixed point of the proposed model, in addition the study demonstrate stability condition of a limit cycle if Burr X AR (1) model have a limit cycle of period greater than one. http://dx.doi.org/10.25130/tjps.24.2019.096