Temporal data mining of uncertain water reservoir data

Mohan, Abhinaya; Révész, Péter

doi:10.1145/2442985.2442987

Cited by 3 publications

(3 citation statements)

References 21 publications

(16 reference statements)

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“…et al [8] for example apply temporal data mining, specifically sliding window technique to segment temporal pattern from reservoir operational database. Mohan and Revesz [9] apply temporal data mining to manage uncertain water reservoir data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir

Mokhtar¹,

Ishak²,

Norwawi³

2016

JOAMS

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Reservoir operation involves many critical decisions due to unpredicted circumstances, such as drought or flood. The reservoir water release decision is one of the challenging tasks for the reservoir operator since the decision to deal with many complicated decision variables and multipurpose operation. Typically, the water that flow into the reservoir comes from the upstream river network. The gauging stations are located at different locations and the distance between the gauging stations and the reservoir are varied. This information indicates that there are spatial relationships between gauging stations and reservoir. This paper aims to investigate the spatial relationship between the upstream gauging stations and the reservoir by mapping temporal rainfall information on each gauging station with the reservoir water level. The mapping was established using Backpropagation Neural Network. The findings show that different location and distance of each gauging station with the Timah Tasoh reservoir does affect the water travel time from gauging stations to the reservoir.  Index Terms-backpropagation neural network, spatialtemporal data, reservoir water level forecasting

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Section: Literature Reviewmentioning

confidence: 99%

“…The ability for analyzing the huge amount of data is still inadequate and there is a need for adapting data mining tools. Currently, spatialtemporal data mining has been widely discussed in reservoir operation and management research [9], [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir

Mokhtar¹,

Ishak²,

Norwawi³

2016

JOAMS

View full text Add to dashboard Cite

show abstract

“…This situation shows that there is a spatial temporal relationship hidden between each gauging station and the reservoir. Currently, there are limited studies that focus on this situation, as most of the previous studies focused on temporal relationship and the total rainfall volume (Mohan & Revesz, 2012;Wan Ishak et al, 2012;Mokhtar et al, 2014). Thus, the spatial temporal relationship needs to be considered for modelling reservoir water release decision.…”

Section: Introductionmentioning

confidence: 99%

Modeling Reservoir Water Release Decision Using Adaptive Neuro Fuzzy Inference System

Mokhtar

Ishak

Norwawi

2016

Journal of Information and Communication Technology

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Reservoir water release decision is one of the critical actions in determining the quantity of water to be retained or released from the reservoir. Typically, the decision is influenced by the reservoir inflow that can be estimated based on the rainfall recorded at the reservoirâ€™s upstream areas. Since the rainfall is recorded at several different locations, the use of temporal pattern alone may not be appropriate. Hence, in this study a spatial temporal pattern was used to retain the spatial information of the rainfallâ€™s location. In addition, rainfall recorded at different locations may cause fuzziness in the data representation. Therefore, a hybrid computational intelligence approach, namely the Adaptive Neuro Fuzzy Inference System (ANFIS), was used to develop a reservoir water release decision model. ANFIS integrates both the neural network and fuzzy logic principles in order to deal with the fuzziness and complexity of the spatial temporal pattern of rainfall. In this study, the Timah Tasoh reservoir and rainfall from five upstream gauging stations were used as a case study. Two ANFIS models were developed and their performances were compared based on the lowest square error achieved from the simulation conducted. Both models utilized the spatial temporal pattern of the rainfall as input. The first model considered the current reservoir water level as an additional input, while the second model retained the existing input. The result indicated that the application of ANFIS could be used successfully for modeling reservoir water release decision. The first model with the additional input showed better performance with the lowest square error compared to the second model.

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K-Medoids Clustering Based Approach to Predict the Future Water Height of a Reservoir

Rushdi

Perera

2018

2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer)

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Temporal data mining of uncertain water reservoir data

Cited by 3 publications

References 21 publications

Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir

Investigating the Spatial Relationship between the Upstream Gauging Stations and the Reservoir

Modeling Reservoir Water Release Decision Using Adaptive Neuro Fuzzy Inference System

K-Medoids Clustering Based Approach to Predict the Future Water Height of a Reservoir

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