Flow forecasting of hirakud reservoir with ARIMA model

Rath, Ashutosh; Samantaray, Sandeep; Bhoi, Kirti Sudha; Swain, Prakash Chandra

doi:10.1109/icecds.2017.8389997

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…The ARIMA or Box-Jenkins method was first introduced in 1970s [22]. It has seen numerous applications in various areas for forecasting processes such as stock prices [23], electricity market [24], computer hardware resource [25,26], natural phenomena [27], etc., that yield time-series data. In Reference [26], ARIMA and long short-term memory (LSTM) recurrent network for forecasting performance of CPU (Central Processing Unit) usage of server machines in data centers are compared.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

Singh

Mahmoud

2019

MAKE

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In this paper, auto-regressive integrated moving average (ARIMA) time-series data forecast models are evaluated to ascertain their feasibility in predicting human–machine interface (HMI) state transitions, which are modeled as multivariate time-series patterns. Human–machine interface states generally include changes in their visually displayed information brought about due to both process parameter changes and user actions. This approach has wide applications in industrial controls, such as nuclear power plant control rooms and transportation industry, such as aircraft cockpits, etc., to develop non-intrusive real-time monitoring solutions for human operator situational awareness and potentially predicting human-in-the-loop error trend precursors.

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Section: Related Workmentioning

confidence: 99%

Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

Singh

Mahmoud

2019

MAKE

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“…where ϕ(L) and Θ(L) are the p-order and q-order characteristic polynomials of the independent variable "L." To conclude, in ARIMA(p, d, q), d is the integral sum order, p is the autoregressive coefficient, and q is the moving average coefficient [32].…”

Section: Arima Modelingmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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“…Every year, substantial public and financial damages, as well as fatalities, are caused by dangerous storms worldwide, specifically in areas subjected to monsoon weather and regions with slow growth of water conservancy schemes ( Jiang et al 2013;Wang et al 2015;Yu et al 2015). Flood prediction and forecasting act as the essential practices to control flood event across the globe (Moore et al 2005;Jiang et al 2016;Rath et al 2017;Samantaray & Sahoo 2020). Evidence of this information is a complex investigation that has concerned researchers over past decades.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India

2021

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Accurateness in flood prediction is of utmost significance for mitigating catastrophes caused by flood events. Flooding leads to severe civic and financial damage, particularly in large river basins, and mainly affects the downstream regions of a river bed. Artificial Intelligence (AI) models have been effectively utilized as a tool for modeling numerous nonlinear relationships and is suitable to model complex hydrological systems. Therefore, the main purpose of this research is to propose an effective hybrid system by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with meta-heuristic Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) for flood prediction in River Mahanadi, India. Robustness of proposed meta-heurestics are assessed by comparing with a conventional ANFIS model focusing on various input combinations considering 50 years of monthly historical flood discharge data. The potential of the AI models is evaluated and compared with observed data in both training and validation sets based on three statistical performance evaluation factors, namely root mean squared error (RMSE), mean squared error (MSE) and Wilmott Index (WI). Results reveal that robust ANFIS-GOA outperforms standalone AI techniques and can make superior flood forecasting for all input scenarios.

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Flow forecasting of hirakud reservoir with ARIMA model

Cited by 7 publications

References 4 publications

Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India

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