Data-mining models for water resource applications
TL;DR: This review paper elaborates the theoretical background of data-mining models and highlights the applications in knowledge data discovery from a water resources database and research gaps have been identified and potential directions towards minimising the gap are identified.
Abstract: Advancements in the field of instrumentation has led to the development of sophisticated apparatus with inbuilt data storage mechanism for measuring water quantity and quality. In water resources, the database has been measured and recorded both in the temporal and spatial scale for many watersheds. Dealing with a large database increases the associated complexity in modelling. The complexity existing in a large database is well represented as simple and understandable expressions by data-mining models/processes. This review paper elaborates the theoretical background of data-mining models and highlights the applications in knowledge data discovery from a water resources database. Based on the understanding/learning from the reported research works, research gaps have been identified and potential directions towards minimising the gap are identified and discussed.
Citations
More filters
TL;DR: An attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules.
Abstract: The persistent problem in reservoir operation is that the derived optimal releases fail to incorporate the decision maker or reservoir operators’ knowledge into reservoir operation models. The reservoir operators’ knowledge is specific to that particular reservoir and incorporating such an experienced knowledge will help to derive field reality based operation rules. The available historical reservoir operation databases are the representative samples of reservoir operators’ knowledge or experience. Thus, an attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules. The developed methodological framework utilizes the strength and capability of recently developed predictive datamining algorithms to recover the knowledge from large historical database. Predictive data-mining algorithms such as a) classifier: Artificial Neural Network (ANN), and b) regression: Support Vector Regression (SVR) have been used for single reservoir operation data-mining (SROD) modelling framework to explore the temporal dependence between different variables of reservoir operation. The rules of operation or knowledge learned from the training database have been used as guiding rules for predicting the future reservoir operators’ decision on operating the reservoir for the given condition on the inflow, initial storage, and demand requirements. The developed SROD model was found to be efficient in exploring the hidden relationships that exist in a single reservoir system.
8 citations
Cites methods from "Data-mining models for water resour..."
...This above goal/task can be achieved by two categories of data mining techniques namely; a) Descriptive Approach, and b) Predictive Approach (Mohan and Ramsundram 2013)....
[...]
01 Jan 2020
TL;DR: In this paper, the authors present a roadmap to the use of artificial intelligence techniques in life cycle inventory (LCI) studies with a focus on AI integration and a framework for using AI in LCI was developed.
Abstract: Energy revolution from the conventional fossil fuels to clean energy is fast gaining traction with renewable and clean energy sources blazing the trail on the global scale. This has consequentially reduced electricity prices in certain countries and reduced carbon footprints in both manufacturing and service industries. Asides the advantages of these clean energy technologies, the assessment of their life cycle has recently gained more attention with life cycle inventory playing a major role. Life cycle inventory is a critical component in life cycle assessment. However, a life cycle inventory study is as accurate as the data used. This study presents a roadmap to the use of artificial intelligence (AI) techniques in life cycle inventory (LCI). The data chain for efficient local resident data availability for LCA studies was considered with a focus on AI integration. In addition, a framework for the use of AI in LCI was developed. The study concluded that it was possible to proffer solution to LCI data unavailability problem using AI with the joint support of public and private partners.
1 citations
References
More filters
01 Jun 1993
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
15,645 citations
"Data-mining models for water resour..." refers methods in this paper
...The descriptive technique involves analysing the database and identifying patterns based on associations or correlations and delivering a description about the database in terms of association rules (Agrawal et al. 1993; Agarwal and Srikant 1994)....
[...]
Proceedings Article•
01 Jul 1998
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Abstract: We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving thii problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database.
10,863 citations
TL;DR: An overview of this emerging field is provided, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases.
Abstract: ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.
4,782 citations
"Data-mining models for water resour..." refers background or methods in this paper
...The development and application of data mining occurred in the recent past for analysing market-based transactions, financial transactions, etc. Fayyad et al. (1996) developed a KDP for application in the field of engineering....
[...]
...To overcome these complexities and to represent the hidden relationships in the large database in the form of simple and understandable rules/ information, a data mining–based knowledge discovery process (KDP) has been developed (Fayyad et al. 1996)....
[...]
...To extract the knowledge/pattern from the transformed data set, Fayyad et al. (1996) proposed a nine-step KDP, which is as follows: step 1: Developing and understanding an application domain step 2: Creating a target database step 3: Preprocessing the data step 4: Reducing and projecting the…...
[...]
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant assoc...
3,198 citations
TL;DR: Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules, to assess the state of the art in optimization of reservoir system management and operations.
Abstract: With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules.
1,484 citations