Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

Supatmi, Sri; R, Hou; Sumitra, Irfan Dwiguna

doi:10.1155/2019/6203510

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…Almost 70% of the whole data is loaded into MATLAB. Generating ANFIS: Next, we implement the ANFIS of the selected Sugeno model, after defining inputs, parameters, and output variables [48]. The ANFIS model's structure consists of input parameters, membership functions of input, and fuzzy rules that are the fuzzy logic's backbone.…”

Section: Anfis For Covid-19mentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

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Section: Anfis For Covid-19mentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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“…e HNFIS is developed by hybridization of the fuzzy inference system (FIS) with artificial neural network (ANN) [30], where the parameters of FIS were optimized using ANN which enables the system to learn the problem systematically and helps to enhance the predictive performance of the model. ANN has a good capacity to learn automatically.…”

Section: Hybrid Neuro Fuzzy Inference System (Hnfis)mentioning

confidence: 99%

“…Some of the ML models have shown their superiority over others in solving environmental prediction problems. ough there is no single method that shows consistent excellence in solving all kinds of environmental problems in many regions, overall it has been noticed that some models often perform better in solving complex prediction problems which include extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Jabbar

Zahra

et al. 2021

Complexity

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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using density-scatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably.

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“…Selanjutnya, dalam menentukan kalor jenis zat menggunakan metode Fuzzy Mamdani. Metode ini akan memberikan toleransi terhadap nilai, apabila terjadi perbedaan sedikit pada suatu nilai tersebut tidak akan memberikan perubahan yang signifikan [9], [10]. Dengan demikian, akan mempermudah dalam menentukan jenis kalornya walaupun hasil yang didapat tidak sama persis.…”

Section: Pendahuluanunclassified

Tutorial Praktikum Kalorimeter Berbasis Augmented Reality Dan Metode Mamdani

Lestari¹,

Supatmi²,

Ishaq³

2020

Komputika

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Proses pembelajaran harus dikemas dengan menarik agar peserta didik termotivasi untuk belajar. Maka dari itu, diperlukan media interaktif yang menunjang proses pembelajaran didukung oleh alat-alat berupa media yang menarik seperti teknologi Augmented Reality. Teknologi ini banyak digunakan untuk industri hiburan karena dapat memberikan kesan nyata kepada penggunanya sehingga dapat membantu meningkatkan motivasi para peserta didik dalam belajar. Pada topik ini, penelitian dikhususkan pada pembuatan Augmented Reality tentang tutorial praktikum fisika dasar modul kalorimeter pada jurusan Sistem Komputer di Universitas Komputer Indonesia yang berbasis Android. Aplikasi ini menyajikan pengenalan alat dan bahan praktikum serta langkah-langkah praktikum. Selain itu, dapat melakukan pemrosesan data praktikum menggunakan metode fuzzy Mamdani karena dalam melakukan pengukuran sering terjadi kesalahan yang mengakibatkan hasilnya tidak tepat sama dengan data kalor jenis zat sehingga kesulitan dalam menentukannya. Hasil pengujian menunjukkan bahwa aplikasi dapat menampilkan objek 3D beserta penjelasannya dan diperoleh error data sebesar 0,0000005 menggunakan metode fuzzy mamdani sehingga dapat digunakan untuk penentuan kalor jenis. Untuk kemunculan objek 3D disarankan berada di intensitas cahaya (52 – 9786 lux) serta jarak antara kamera AR dengan marker berada pada 10 cm sampai 70 cm dan sudut 00 sampai 600 dengan 50% marker tidak terhalang.

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Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

Cited by 15 publications

References 28 publications

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem

Tutorial Praktikum Kalorimeter Berbasis Augmented Reality Dan Metode Mamdani

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