Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach

Nguyen-Ky, Tai; Mushtaq, Shahbaz; Loch, Adam; Reardon‐Smith, Kathryn; An-Vo, Duc-Anh; Ngo-Cong, Duc; Tran-Cong, T.

doi:10.1016/j.jhydrol.2017.11.049

Cited by 28 publications

(18 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By analyzing the result of economic forecast, government departments or individuals can make necessary adjustments and interventions to economic activities, and adopt the corresponding economic measures to make economic activities go where harm can be avoided and lead to the development of economic activities. The forecast result does not coincide with the actual economic operations [13] [14].…”

Section: Overall Architecturementioning

confidence: 76%

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

He¹

2020

Preprint

View full text Add to dashboard Cite

As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction, for instance.Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Meanwhile, this method can also choose the optimal neural network prediction model at an adaptive learning rate so as to make the prediction and analysis result more accurate.

show abstract

Section: Overall Architecturementioning

confidence: 76%

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

He¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, ABMs fail to store the memory of agents when undertaking similar decisions in the future, which is one of the major intrinsic factors in the decision‐making process. Machine learning techniques could be used to store the memory of the agents' past decisions (Du, Cai, Brozovi, & Minsker, ; Nguyen‐ky et al, ). Du et al () tried to model irrigators' water trade decisions using ABM and machine learning for a hypothetical water market scenario.…”

Section: Discussionmentioning

confidence: 99%

Reviewing the decision‐making behavior of irrigators

et al. 2019

Self Cite

View full text Add to dashboard Cite

The contribution of agriculture to society is undeniable, as is its impact on the environment. Irrigators' decisions to follow best management practices or implement a policy change, to accept a technology, or even to exit farming, all affect society. Hence the decision-making behavior of irrigators is of interest to politicians, policymakers, and researchers due to their impact on resource use and social concerns for their welfare. There are numerous studies available regarding the decision-making behavior of irrigators. Most of them concentrate on decisions within a single time frame, single decisions with multiple driving forces, or multiple decisions with a single driving force. We have conducted a comprehensive review of the existing literature related to irrigators' decision-making behavior. We used a systematic method to identify relevant publications and used qualitative data analysis (content analysis) to analyze trends and/or patterns across the selected articles. This research provided a typology and an overarching high-level framework of irrigators' decision-making process irrespective of the types of decisions made.The results of the study demonstrate that it is highly beneficial to integrate both qualitative and quantitative methods in a single study to get a complete picture of irrigators' decision-making process. This allows us to ensure that we have captured the relevant drivers of decision-making in highly dynamic and complex environments. Better knowledge of irrigators' decision-making process allows regulators to shape improved agricultural policy and increase acceptance by irrigators of technologies that allow water managers to allocate resources fairly among different stakeholders. This article is categorized under: Human Water > Methods Engineering Water > Planning Water Human Water > Water Governance K E Y W O R D S decision-making framework, irrigators' decision-making behavior, qualitative and quantitative research methods, qualitative content analysis

show abstract

“…ANN is a method inspired by the way the human brain processes data, and emulates its functionality by using similar operations and connectivity as a biological neural system [29,30,68]. Recently, ANN models have been widely utilized in water resources and hydrology applications because of its ability to extract complex nonlinear relationships, which exist within the hydrology data [30,31].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

Zubaidi

Ortega‐Martorell

Al-Bugharbee

et al. 2020

Water

145

View full text Add to dashboard Cite

The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Moreover, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth.

show abstract

Predicting water allocation trade prices using a hybrid Artificial Neural Network-Bayesian modelling approach

Cited by 28 publications

References 45 publications

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

Reviewing the decision‐making behavior of irrigators

Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

Contact Info

Product

Resources

About