Inadequate agricultural planning compounded by inaccurate predictions results in an inflated loca... more Inadequate agricultural planning compounded by inaccurate predictions results in an inflated local market rate and prompts higher importation of wheat. To tackle this problem, this research has designed two-phase universal machine learning (ML) model to predict wheat yield (Wpred), utilizing 27 agricultural counties’ data within the Agro-ecological zone. The universal model, online sequential extreme learning machines coupled with ant colony optimization (ACO-OSELM) is developed, by incorporating the significant annual yield data lagged at (t − 1) as the model’s predictor to generate future yield at 6 test stations. In the first phase, ACO is adopted to search for suitable, statistically relevant data stations for model training, and the corresponding test station by virtue of a feature selection strategy. An annual wheat yield time-series input dataset is constructed utilizing data from each selected training station (1981–2013) and applied against 6 test stations (with each case m...
2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2015
This research focuses on surveying in an attractive field of quantum computing. The paper begins ... more This research focuses on surveying in an attractive field of quantum computing. The paper begins by highlighting a brief history of quantum mechanics. Major elements of quantum computing such as quantum superposition, quantum tunnelling and qubits are addressed at next from a physics perspective. In addition, various methods and applications of quantum physics are also examined. This paper discusses the power and efficiencies that a quantum computer provides and the basis for these claims. Furthermore, the level of research in quantum computing and it's commercial markets assays to find out the major contributions and developments in the field of quantum computing. The top two leading organisations in quantum computing are picked and reviewed with their up to date contributions. This paper expresses the methods and techniques which are being used by these two organisations to implement a quantum processor and the level of success that has been achieved. This research attempts to log the challenges and limitations that these organisations face in the development of quantum computing. Finally, the research compares quantum model with classical computing model.
The utilization of renewable energy is an essential tool for the mitigation of the negative impac... more The utilization of renewable energy is an essential tool for the mitigation of the negative impacts of a changing climate on water resources, ecosystems and human lives. However, the intermittent nature of renewable energy poses a practical challenge for its wider applicability such as in electrical power grid utilization. Accurate modeling and forecasting of renewable energy resources, such as wind and solar energy are necessary to make the energy generation process easy and relatively reliable for utilization in grid energy systems. As the development of physics-based models can be economically costly and includes many assumptions and constraints, the emergence of data-driven and machine learning modeling approaches are becoming attractive and viable alternative tools. This chapter outlines the respective phases required for the development of machine learning models in the renewable energy generation sector, including the pertinent concepts and definitions that are used in time-s...
The nature of streamflow in the basins is stochastic and complex making it difficult to make an a... more The nature of streamflow in the basins is stochastic and complex making it difficult to make an accurate prediction about the future river flows. Recently, artificial neural-based deep learning models with a nonlinear structure have become predominant in water engineering forecasting problems such as river flow predictions. In this study, we investigate the potential of Singular Spectral Analysis (SSA), Seasonal-Trend decomposition using Loess (STL) and attribute selection pre-processing approaches with the neural network methods in predicting monthly river streamflows in the Nallihan stream, Turkey. Antecedent measured streamflow, precipitation, relative humidity and temperature data between the years 1996 and 2016 from the observing stations in the basin boundaries were used as model inputs under different scenarios using the correlations between the past measured variables, to predict one-step-ahead flow data. To compare the newer hybrid model performances; evaluation metrics inc...
Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life... more Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life. This chapter designs M5 tree-based machine learning model integrated with advanced multivariate empirical mode decomposition (i.e., MEMD-M5 Tree) for daily flood index (FI) forecasting for Lockyer Valley in southeast Queensland, Australia, using data from January 01, 1950, to December 31, 2012. The MEMD-M5 tree is evaluated against MEMD-RF, standalone M5 tree, and RF models via statistical metrics, diagnostic plots with error distributions between simulated and observed daily flood index. The results indicate that MEMD-M5 tree outperforms the comparative models by attaining maximum values of r = 0.990, WI = 0.992, ENS = 0.979, and L = 0.920. The MEMD-M5 tree outperforms other models by registering the least value of RMSE and MAE and can precisely emulate 97.94% of daily FI value. Graphical diagnostic analysis and forecast error histograms further reveal that the MEMD-M5 tree has a grea...
Electricity, considered to be the lifeblood of industrialized societies, is underpinned by a vast... more Electricity, considered to be the lifeblood of industrialized societies, is underpinned by a vastly complex and interconnected grid consisting of multiple stakeholders. In this industry, the accurate forecasting of electricity load has become vital to planning, operations, transmission safety, energy transaction planning, and economic dispatch. However, accurate forecasting is a challenge due to a range of uncertainties with weather conditions and extreme weather events chief among them. The forecasting challenge is magnified by the existence of urban climate phenomena such as the Urban Heat Islands. These have the effect of intensifying weather conditions and changing electricity demand patterns, especially during summer months. Intending to address this challenge, we evaluate a hybrid model, the winner-take-all emotional neural network, against random forest and multiple linear regression models for the short-term electricity demand prediction utilizing air temperature data from f...
Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of... more Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources ma...
Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Ni... more Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Nino–Southern Oscillation (ENSO). The chapter applies an advanced statistical copula approach to model lag relationships between monthly Southern Oscillation Index (SOI), an ENSO indicator, and monthly Flood Index (FI) that can be used for flood prediction. Copula parameters were numerically derived from under a hybrid-evolution Markov chain Monte Carlo (MCMC) approach within a Bayesian framework. The empirical findings showed that monthly SOI data from Aug to Dec have a significant correlation with monthly FI that can be predicted at least four months ahead using SOI information. These advanced flood prediction models, presented in this chapter, are indeed imperative tools for civil protection and important to early warning and risk reduction systems.
Inadequate agricultural planning compounded by inaccurate predictions results in an inflated loca... more Inadequate agricultural planning compounded by inaccurate predictions results in an inflated local market rate and prompts higher importation of wheat. To tackle this problem, this research has designed two-phase universal machine learning (ML) model to predict wheat yield (Wpred), utilizing 27 agricultural counties’ data within the Agro-ecological zone. The universal model, online sequential extreme learning machines coupled with ant colony optimization (ACO-OSELM) is developed, by incorporating the significant annual yield data lagged at (t − 1) as the model’s predictor to generate future yield at 6 test stations. In the first phase, ACO is adopted to search for suitable, statistically relevant data stations for model training, and the corresponding test station by virtue of a feature selection strategy. An annual wheat yield time-series input dataset is constructed utilizing data from each selected training station (1981–2013) and applied against 6 test stations (with each case m...
2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2015
This research focuses on surveying in an attractive field of quantum computing. The paper begins ... more This research focuses on surveying in an attractive field of quantum computing. The paper begins by highlighting a brief history of quantum mechanics. Major elements of quantum computing such as quantum superposition, quantum tunnelling and qubits are addressed at next from a physics perspective. In addition, various methods and applications of quantum physics are also examined. This paper discusses the power and efficiencies that a quantum computer provides and the basis for these claims. Furthermore, the level of research in quantum computing and it's commercial markets assays to find out the major contributions and developments in the field of quantum computing. The top two leading organisations in quantum computing are picked and reviewed with their up to date contributions. This paper expresses the methods and techniques which are being used by these two organisations to implement a quantum processor and the level of success that has been achieved. This research attempts to log the challenges and limitations that these organisations face in the development of quantum computing. Finally, the research compares quantum model with classical computing model.
The utilization of renewable energy is an essential tool for the mitigation of the negative impac... more The utilization of renewable energy is an essential tool for the mitigation of the negative impacts of a changing climate on water resources, ecosystems and human lives. However, the intermittent nature of renewable energy poses a practical challenge for its wider applicability such as in electrical power grid utilization. Accurate modeling and forecasting of renewable energy resources, such as wind and solar energy are necessary to make the energy generation process easy and relatively reliable for utilization in grid energy systems. As the development of physics-based models can be economically costly and includes many assumptions and constraints, the emergence of data-driven and machine learning modeling approaches are becoming attractive and viable alternative tools. This chapter outlines the respective phases required for the development of machine learning models in the renewable energy generation sector, including the pertinent concepts and definitions that are used in time-s...
The nature of streamflow in the basins is stochastic and complex making it difficult to make an a... more The nature of streamflow in the basins is stochastic and complex making it difficult to make an accurate prediction about the future river flows. Recently, artificial neural-based deep learning models with a nonlinear structure have become predominant in water engineering forecasting problems such as river flow predictions. In this study, we investigate the potential of Singular Spectral Analysis (SSA), Seasonal-Trend decomposition using Loess (STL) and attribute selection pre-processing approaches with the neural network methods in predicting monthly river streamflows in the Nallihan stream, Turkey. Antecedent measured streamflow, precipitation, relative humidity and temperature data between the years 1996 and 2016 from the observing stations in the basin boundaries were used as model inputs under different scenarios using the correlations between the past measured variables, to predict one-step-ahead flow data. To compare the newer hybrid model performances; evaluation metrics inc...
Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life... more Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life. This chapter designs M5 tree-based machine learning model integrated with advanced multivariate empirical mode decomposition (i.e., MEMD-M5 Tree) for daily flood index (FI) forecasting for Lockyer Valley in southeast Queensland, Australia, using data from January 01, 1950, to December 31, 2012. The MEMD-M5 tree is evaluated against MEMD-RF, standalone M5 tree, and RF models via statistical metrics, diagnostic plots with error distributions between simulated and observed daily flood index. The results indicate that MEMD-M5 tree outperforms the comparative models by attaining maximum values of r = 0.990, WI = 0.992, ENS = 0.979, and L = 0.920. The MEMD-M5 tree outperforms other models by registering the least value of RMSE and MAE and can precisely emulate 97.94% of daily FI value. Graphical diagnostic analysis and forecast error histograms further reveal that the MEMD-M5 tree has a grea...
Electricity, considered to be the lifeblood of industrialized societies, is underpinned by a vast... more Electricity, considered to be the lifeblood of industrialized societies, is underpinned by a vastly complex and interconnected grid consisting of multiple stakeholders. In this industry, the accurate forecasting of electricity load has become vital to planning, operations, transmission safety, energy transaction planning, and economic dispatch. However, accurate forecasting is a challenge due to a range of uncertainties with weather conditions and extreme weather events chief among them. The forecasting challenge is magnified by the existence of urban climate phenomena such as the Urban Heat Islands. These have the effect of intensifying weather conditions and changing electricity demand patterns, especially during summer months. Intending to address this challenge, we evaluate a hybrid model, the winner-take-all emotional neural network, against random forest and multiple linear regression models for the short-term electricity demand prediction utilizing air temperature data from f...
Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of... more Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources ma...
Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Ni... more Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Nino–Southern Oscillation (ENSO). The chapter applies an advanced statistical copula approach to model lag relationships between monthly Southern Oscillation Index (SOI), an ENSO indicator, and monthly Flood Index (FI) that can be used for flood prediction. Copula parameters were numerically derived from under a hybrid-evolution Markov chain Monte Carlo (MCMC) approach within a Bayesian framework. The empirical findings showed that monthly SOI data from Aug to Dec have a significant correlation with monthly FI that can be predicted at least four months ahead using SOI information. These advanced flood prediction models, presented in this chapter, are indeed imperative tools for civil protection and important to early warning and risk reduction systems.
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Papers by Ramendra Prasad