Rainfall events prediction using rule-based fuzzy inference system

Asklany, Somia; Elhelow, Khaled; Youssef, I. K.; El-Wahab, Mustafa Abd

doi:10.1016/j.atmosres.2011.02.015

Cited by 65 publications

(29 citation statements)

References 27 publications

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“…The ML models utilize, assimilate and 'learn' from the evidence of past climate trends using observational dataset to predict the future. Many types of ML algorithms have recently been proposed in literature, including the co-integration methods that analyze relationships between stationary and nonstationary data (Kaufmann et al, 2011;Kaufmann and Stern, 2002), regression approaches for evaluating timeseries properties of air temperature (Douglass et al, 2004;Stone and Allen, 2005), neural networks for predicting rainfall Marohasy, 2012, 2014), wavelet or vector-regression for hydro-meteorological forecasting (Belayneh and Adamowski, 2012) and rainfall events prediction using rule-based fuzzy inference systems (Asklany et al, 2011). The practical advantages of the ML algorithm over the GCM are the explanation of the externally driven climate without the need for complex physical models, easiness of experimentation, validation and evaluation, low computational burden, much more simple and fast in the training and the testing phases, the applicability to the data for a specific point of measurement (a specific area, for example) and the competitive performance compared to physical models (Ortiz-García et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

260

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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Section: Introductionmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

260

102

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“…Beberapa peneliti menggunakan metode Logika Fuzzy untuk menghadapi berbagai kondisi yang tidak pasti/jelas (Asklany et al, 2011;FallahGhalhary et al, 2009;Hasan et al, 2013) sedangkan beberapa peneliti yang lain menggunakan metode Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat (Awan and Maqbool, 2010;Khidir et al, 2013;Mislan et al, 2015).…”

Section: Tinjauan Pustakaunclassified

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur

Utomo

Mahmudy

Anam

2017

JTIIK

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AbstrakPrakiraan curah hujan merupakan salah satu tanggung jawab penting yang dilakukan oleh layanan meteorologi di seluruh dunia. Permasalahan utama dalam hal analisis dan prakiraan adalah tingkat kesalahan yang semakin meningkat dari waktu ke waktu. Hal ini dapat terjadi karena kondisi ketidakpastian juga meningkat seiring dengan perubahan musim dan iklim. Penelitian ini mencoba mengombinasikan dua metode yaitu Logika Fuzzy untuk menghadapi kondisi-kondisi yang tidak pasti dan Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat. Penelitian ini juga menggunakan algoritma Particle Swarm Optimization untuk menentukan kebutuhan secara otomatis. Kebutuhan yang perlu ditentukan secara otomatis adalah bobot-bobot awal dalam Jaringan Syaraf Tiruan multi-layer sebelum akhirnya melakukan proses pelatihan algoritma. Penelitian ini menggunakan studi kasus di empat area Jawa Timur yaitu Puspo, Tutur, Tosari, dan Sumber untuk memprakirakan curah hujan di area Puspo. Data yang digunakan merupakan curah hujan timeseries yang dicatat selama 10 tahun oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil penelitian ini menunjukkan bahwa kombinasi dari Logika Fuzzy dengan Jaringan Syaraf Tiruan multi-layer mampu memberikan tingkat RMSE sebesar 2.399 dibandingkan dengan hanya menggunakan regresi linear dengan tingkat RMSE sebesar 7.211. Kata kunci: fuzzy, hujan, hybrid, jaringan syaraf, optimasi, timeseries Abstract Rainfall forecasting is one of the important responsibilities that carried out by meteorological services in the worldwide. The main problem in terms of analysis and forecasting is

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“…In general, there are two kinds of fuzzy inference systems that can be implemented, namely the Mamdani Type and the Sugeno Type. According to [8]; [9], there are four phases to obtain output in the type of Mamdani Fuzzy Inference System, namely: 1. Comparing the input variables with membership functions in the antecedent ("because" part) to obtain the membership value of each linguistic variables.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Development of erosion risk map using fuzzy logic approach

et al. 2017

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Abstract. Erosion-hazard assessment is an important aspect in the management of a river basin such as Siak River Basin, Riau Province, Indonesia. This study presents an application of fuzzy logic approach to develop erosion risk map based on geographic information system. Fuzzy logic is a computing approach based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The results of the erosion risk map were verified by using field measurements. The verification result shows that the parameter of soil-erodibility (K) indicates a good agreement with field measurement data. The classification of soil-erodibility (K) as the result of validation were: very low (0.0-0.1), medium (0.21-0.32), high (0.44-0.55) and very high (0.56-0.64). The results obtained from this study show that the erosion risk map of Siak River Basin were dominantly classified as medium level which cover about 68.54%. The other classifications were high and very low erosion level which cover about 28.84% and 2.61% respectively.

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Rainfall events prediction using rule-based fuzzy inference system

Cited by 65 publications

References 27 publications

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur

Development of erosion risk map using fuzzy logic approach

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