A modular technique for monthly rainfall time series prediction

Kaiornrit, Jesada; Wong, Kok Wai; Fung, Chun Che

doi:10.1109/cidue.2013.6595775

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…One of the well-known computational intelligence techniques used for modelling reservoir water release decision and forecasting is the Artificial Neural Network (ANN) (Nazri et al, 2013;Mokhtar et al, 2014;Wan Ishak et al, 2015). However, this technique suffers from poor interpretability, since it is difficult for humans to explain the practicality and logical meaning behind the learned weights of the model (Jothiprakash & Kote, 2011;Kajornrit et al, 2013). This problem can be solved by the Adaptive Neuro Fuzzy Inference System (ANFIS).…”

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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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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“…Another study presented as modular technique to predict monthly rainfall using time series model [15]. This work is based on two layers for forecasting in order to understand the link between the input and output parameters of the rainfall regime.…”

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confidence: 99%

Automating Time Series Analysis to Predict/Forecast Rainfall in AGUELMAM SIDI ALI Watershed in Morocco

Chalh¹,

Bakkoury²,

Ouazar³

et al. 2019

IJITEE

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Moroccan economy is largely based upon rainfall, use of water resources and crop productivity, for that it’s considered as an agricultural country. It’s more required and more important for any farmer to forecast rainfall prediction in order to analyze crop productivity. Predicting the atmosphere or forecasting the state of the weather is considered as challenge for scientific research. The prediction of rainfall monthly or/and seasonal time scales is the application of science and technology to invent and to schedule the agriculture strategies. Recently different research articles achieve to forecast and/or predict rainfall monthly or seasonal time scales using different techniques. The methodology followed in this work, be focused on automating time series analysis to forecast / predict precipitation daily, monthly or seasonal in Aguelmam Sidi Ali basin in Morocco for last 32 years ago from 1975 to 2007. We first have to study the rainfall data theoretically using the simplest form statistical analysis, which is the univariate analysis, as long as only one variable is involved in our case study. To get the selected and suitable model of time series to automate, we used different autocorrelation methods based on various criterion such as: Akaike Information Criterion (AIC), estimation of parameters using Yule-Walker (YW) and Maximum Likelihood Estimation (MLE). The results of our experiment show that it is possible using our system to obtain accurate rainfall prediction, with a more details and with a very fast way. It shows also that it’s possible to predict for next months or next years. To minimize the risk of floods and natural disasters within a basin in general and within the Aguelmam Sidi Ali basin in particular, accurate and timely rainfall forecasting is required.

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Two-Level Multivariate Fuzzy Logic Based Integrated Model for Monsoon Rainfall Prediction

Bose

Mali

2015

Advances in Intelligent Systems and Computing

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A modular technique for monthly rainfall time series prediction

Cited by 6 publications

References 16 publications

Modeling Reservoir Water Release Decision Using Adaptive Neuro Fuzzy Inference System

Modeling Reservoir Water Release Decision Using Adaptive Neuro Fuzzy Inference System

Automating Time Series Analysis to Predict/Forecast Rainfall in AGUELMAM SIDI ALI Watershed in Morocco

Two-Level Multivariate Fuzzy Logic Based Integrated Model for Monsoon Rainfall Prediction

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