Journal ArticleDOI
Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques
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TLDR
The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.About:
This article is published in Journal of Hydrology.The article was published on 2012-06-06. It has received 176 citations till now. The article focuses on the topics: Adaptive neuro fuzzy inference system & Neuro-fuzzy.read more
Citations
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Journal ArticleDOI
Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
TL;DR: In this paper, support vector machine (SVM) is used to predict flood susceptibility in the Kuala Terengganu basin, Malaysia, and four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were used to check the robustness of the SVM model.
Journal ArticleDOI
Flood prediction using machine learning models: Literature review
TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.
Journal ArticleDOI
Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS
Dieu Tien Bui,Biswajeet Pradhan,Biswajeet Pradhan,Haleh Nampak,Quang-Thanh Bui,Quynh-An Tran,Quoc-Phi Nguyen +6 more
TL;DR: The results show that the proposedMONF model outperforms the above benchmark models; it is concluded that the MONF model is a new alternative tool that should be used in flood susceptibility mapping.
Journal ArticleDOI
Flood Prediction Using Machine Learning Models: Literature Review
TL;DR: In this paper, the state of the art of ML models in flood prediction and to give insight into the most suitable models are presented. And the major trends in improving the quality of the flood prediction models are investigated.
Journal ArticleDOI
Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods.
TL;DR: It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Related Papers (5)
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more