Rainfall Events Evaluation Using Adaptive Neural-Fuzzy Inference System

Niksaz, Pejman; Latif, Ali Mohammad

doi:10.5815/ijitcs.2014.09.06

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…The membership for sample y in Figure 3C,D is the same if the distribution of samples outside the hypersphere is ignored and the same value 𝜇 0 is utilized. The average radius d 0 (d 0 > R) between samples and their class center is determined as Equation (7), which displays the overall distribution of samples outside the hypersphere, to separate sample y's membership in Figure 3C from sample y's membership in Figure 3D.…”

Section: 22mentioning

confidence: 99%

“…Each year, many persons die or are displaced due to heavy rain and flooding. 7 As a result, early rainfall prediction has been one of the most difficult undertakings globally in recent years. 8 Rainfall seems to be a complex atmospheric phe-nomenon impacted by several climatic factors.…”

Section: Introductionmentioning

confidence: 99%

“…Heat, relative humidity, dew point, and wind are a few extra atmospheric constraints. Each year, many persons die or are displaced due to heavy rain and flooding 7 . As a result, early rainfall prediction has been one of the most difficult undertakings globally in recent years 8 .…”

Section: Introductionmentioning

confidence: 99%

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A novel fuzzy based deep neural network for rain fall prediction using cloud images

Manoj¹,

Harshavardhanan

Victor

et al. 2022

Concurrency and Computation

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Summary The most challenging aspect of weather forecasting is predicting rainfall. In contrast, timely and precise rainfall forecasting is immensely useful in developing effective security measures for active infrastructure projects, logistics, agricultural activities, air transport operations, and flood scenarios. Artificial intelligence has been established to predict rain precipitation, but very little research has been conducted on images than time‐series data to predict rainfall. However, the accuracy of the prediction model utilizing pictures is lower when compared to time series data. Thus a novel fuzzy based deep neural network (NFDNN) model was created to increase rainfall prediction performance. Initially, our research utilized input cloud images, in which the fuzzy wavelet incorporated with neural networks is utilized to identify clouds in the sky. Second, the types of clouds are determined by a fuzzy deep neural networks cloud classifier in which the adaptive fuzzy sparse representation is used. Then, the density‐based spatial clustering for applications containing noise clustering method was used to detect rainy clouds by evaluating the cloud's color and density from the fuzzy deep neural network cloud classifier. Finally, rainfall was predicted using information such as cloud categorization, appearance, and height obtained from cloud position and cloud types. As a result, the proposed NFDNN model gives a better prediction result than the existing methods.

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Section: 22mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel fuzzy based deep neural network for rain fall prediction using cloud images

Manoj¹,

Harshavardhanan

Victor

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Their experimental parameters were humidity, temperature, and rainfall; according to statistical analysis, they were made membership function for each variable [13]. It is inferred that it is not mandatory to have a demarcation line for the classification of the forecasting objects [15,16]; for instance, rainfall prediction was divided into three grades rareness, normality, and plenty of rainfall respectively; thus, said to be fuzziness have followed IF-THEN rule base, for instance, if WP is very high AND TP is lower THEN RF is moderate in which very high, lower and moderate were linguistic variables [9][10]. Fuzzy rules could easily be programmed and fuzzy models can be transparent since rules are based on structure.…”

Section: Introductionmentioning

confidence: 99%

Mamdani Fuzzy Based Prediction of Rainfall Fuzzy Rainfall Index

Selvaraj¹,

Arasan²,

Tamane³

et al. 2023

Advances in Computer Science Research

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This study attempts to investigate the link between rainfall and climatic large-scale synoptic patterns. Here, we employed the fuzzy inference system (FIS) to forecast rainfall. Six linguistic variables were considered as an input for the proposed system, split into two FIS. Average Mean Temperature (degree celsius), Mean Wind velocity (KM-PH), and Evapotranspiration (mm/day) belong to FIS1. In contrast, Relative humidity (%), Season, and Mean sun shines (hrs/day) belong to FIS2, and each model has three triangular membership functions, excluding the season variable. It has four bi-membership functions: summer, winter, South West, and North East. The output models have four memberships function Very high, High, Normal, Low, and Very Low for FIS1 and FIS2; respectively. The IF-THEN rules were assigned based on the individual significance of linguistic variables for rainfall and from expert opinion for the model FIS1, FIS2 and FRFI, In this article, we had implemented 72 numbers of possible rules, moreover model results can be reviewed into surface 3D-Plot. The implication of each variable with output such as FIS1, FIS2, and FRFI was addressed. Finally, predicted values were compared with the actual rainfall data. Thus, the proposed model would be expected rainfall at a reasonable accuracy. All the implementation has been done in MATLAB7 Fuzzy toolbox.

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“…But the choice of machine learning algorithm to solve a problem always depends on the size, quality, and nature of the data (Djam et al, 2011). To produce powerful computing systems, artificial intelligence technologies have a natural effectiveness that can be exploited (Niksaz and Mohammad , 2014).…”

Section: Choice Of Predicting Modelmentioning

confidence: 99%

Symptomatic and Climatic Based Malaria Threat Detection Using Multilevel Thresholding FeedForward Neural Network

A.¹,

R.²

2017

IJITCS

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Abstract-Recent worldwide medical research is focusing on new intelligence approaches for diagnosis of various infections. The sporadic occurrence of malaria diseases in human has pushed the need to develop computational approaches for its diagnoses. Most existing conventional malaria models for classification problems examine the dynamics of asymptomatic and morphological characteristics of malaria parasite in the thick blood smear, but this study examine the symptomatic characteristics of malaria parasite combined with effects of climatic factor which is a great determinant of malaria severity. The need to predict the occurrence of malaria disease and its outbreak will be helpful to take appropriate actions by individuals, World Health Organizations and Government Agencies and its devastating impact will be reduced. This paper proposed Feed-Forward Back-Propagation (FF_BP) Neural Network model to determine the rate of malaria transmission. Monthly averages of climatic factors; rainfall, temperature and relative humidity with monthly malaria incidences were used as input variables. An optimum threshold value of 0.7100 with classification accuracy 87.56%, sensitivity 96.67% and specificity 76.67% and mean square error of 0.100 were achieved. While, the model malaria threat detection rate was 87.56%, positive predictive value was 89.23%, negative predictive value was 92.00% and the standard deviation is 2.533. The statistical analysis of Feed-Forward BackPropagation Neural Network model was conducted and its results were compared with other existing models to check its robustness and viability.

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Rainfall Events Evaluation Using Adaptive Neural-Fuzzy Inference System

Cited by 8 publications

References 24 publications

A novel fuzzy based deep neural network for rain fall prediction using cloud images

A novel fuzzy based deep neural network for rain fall prediction using cloud images

Mamdani Fuzzy Based Prediction of Rainfall Fuzzy Rainfall Index

Symptomatic and Climatic Based Malaria Threat Detection Using Multilevel Thresholding FeedForward Neural Network

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