Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

Abstract

This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

“…At time T, the candidate cell state 𝐶 ̃ is defined as： 𝑪 ̃𝒕 = 𝒕𝒂𝒏𝒉(𝑾 𝑪 ⋅ [𝒉 𝒕−𝟏 , 𝒙 𝒕 ] + 𝒃 𝑪 ) (15) At time T, the current cell state 𝐶 𝑡 is defined as: 𝑪 𝒕 = 𝒇 𝒕 * 𝑪 𝒕−𝟏 + 𝑰 𝒕 * 𝑪 ̃𝒕 (16) The output gate 𝑄 𝑡 outputs the current time step's state information and determines the value of the next hidden state：…”

“…Experimental results indicated that the ensemble model exhibited better prediction accuracy and applicability than individual models. Khanesar et al [15] employed interval type 2 fuzzy logic systems with support vector regression to identify the prediction interval associated with data. In the proposed approach, a penalty term was added to the cost functions to exert full control over the width of the prediction interval.…”

Enhancing Positional Accuracy of the XY-Linear Stage Using Laser Tracker Feedback and IT2FLS

“…The accuracy of this method is lower than the enhanced Karnik-Mendel and higher than other approximate models of Biglarbegian-Melek-Mendel [24] and Nie-Tan [25,26]. The computational complexity for the Maclaurin-based first-order approximator is less than the EKM model, as it does not necessitate the sorting procedure required by EKM [27,28]. The Maclaurin-series-expansion-based first-order approximate output of the IT2FLS is as follows [27,28]:…”

“…The computational complexity for the Maclaurin-based first-order approximator is less than the EKM model, as it does not necessitate the sorting procedure required by EKM [27,28]. The Maclaurin-series-expansion-based first-order approximate output of the IT2FLS is as follows [27,28]:…”

Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach

“…The first 80% of the generated data is used for training and the last 20% is chosen for testing purposes. The comparison results with several other methods including Type-1 TSK FNS [27], Type-2 TSK FNS [27], Feedorward Type-2 FNN [11], SIT2FNN [28], SEIT2 FNN [29], TSCIT2FNN [30], IT2 FNN-GD [26], IT2 FNN-SMC [26], IT2 FNNPSO+ SMC [26], IT2 IFLS -DEKF+GD [11], and IT2FLS with Modified SVR [31] are presented in Table 1,where results support the idea that the proposed approach is effective at system identification by outperforming the other tested algorithms. The behavior of the proposed identification system for the training and tesing data is presented in Figures 3 and 4, respectively.…”