Rainfall Prediction in Lahore City using Data Mining Techniques

Aftab, Shabib; Ahmad, Munir; Hameed, Noureen; Bashir, Muhammad Salman; Ali, Iftikhar; Nawaz, Zahid

doi:10.14569/ijacsa.2018.090439

Cited by 36 publications

(27 citation statements)

References 25 publications

(28 reference statements)

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“…Supervised machine learning techniques need the pre-classified data (training data) for training. During the training process these techniques make rules to classify the unseen data (test data) [18][19], [20][21][22][23], [26][27]. In this study, NASA's clean software defect datasets are used for experiments, including: CM1, JM1, KC1, KC3, MC1, MC2, MW1, PC1, PC2, PC3, PC4 and PC5.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Iqbal¹,

Aftab²,

Ali³

et al. 2019

IJACSA

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Defect prediction at early stages of software development life cycle is a crucial activity of quality assurance process and has been broadly studied in the last two decades. The early prediction of defective modules in developing software can help the development team to utilize the available resources efficiently and effectively to deliver high quality software product in limited time. Until now, many researchers have developed defect prediction models by using machine learning and statistical techniques. Machine learning approach is an effective way to identify the defective modules, which works by extracting the hidden patterns among software attributes. In this study, several machine learning classification techniques are used to predict the software defects in twelve widely used NASA datasets. The classification techniques include: Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF). Performance of used classification techniques is evaluated by using various measures such as: Precision, Recall, F-Measure, Accuracy, MCC, and ROC Area. The detailed results in this research can be used as a baseline for other researches so that any claim regarding the improvement in prediction through any new technique, model or framework can be compared and verified.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Iqbal¹,

Aftab²,

Ali³

et al. 2019

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently Aftab S et. al [2], B. Kavitha Rani & A. Govardhan [4] applied data mining techniques, Edwin, A. I and Martins O.Y. [9] applied decomposition technique with ARIMA model and periodic autoregressive model scheme, Sopipan N. [31] applied ARIMA model, Exponential Smoothing and Holt Winter model and many more [8], [20].…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Periodic Autoregressive Time Series Models Used for Forecasting Analysis of Rainfall Data

Kaur¹,

Rakshit²

2019

IJARET

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The amount of rainfall received over an area is an important factor in assessing availability of water to meet various demands for agriculture, industry, irrigation, generation of hydroelectricity and other human activities. In our study, we consider seasonal and periodic time series models for statistical analysis of rainfall data of Punjab, India. In this research paper we apply the Seasonal Autoregressive Integrated Moving Average and Periodic autoregressive model to analyse the rainfall data of Punjab. For evaluation of the model identification and periodic stationarity the statistical tool used are PeACF and PePACF. For model comparison we use Root mean square percentage error and forecast encompassing test. The results of this research will provide local authorities to develop strategic plans and appropriate use of available water resources.

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“…Time series data is collected over a specific period of time such as hourly, daily, weekly, monthly, quarterly or yearly [23], [40]. Data mining techniques can use this data to predict upcoming situations in various domains such as climate change, education, and finance etc.…”

Section: Introductionmentioning

confidence: 99%

“…Data mining techniques can use this data to predict upcoming situations in various domains such as climate change, education, and finance etc. These techniques can be used to extract hidden knowledge from time series data for future use [23], [27], [29], [40]. Weather forecasting is very benificial but challenging task [26].…”

Section: Introductionmentioning

confidence: 99%

“…Weather data consists of various atmospheric features such as wind speed, humidity, pressure and temperature etc. Data mining techniques have the capacity to extract the hidden patterns among available features of past weather data and then these techniques can predict future weather conditions by using extracted patterns [40]. Rainfall is a complex atmospheric process, which depends upon many weather related features.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Aftab¹,

Ahmad²,

Hameed³

et al. 2018

ijacsa

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Abstract-Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in advance regarding: ongoing construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. Published papers from year 2013 to 2017 from renowned online search libraries are considered for this research. This review will serve the researchers to analyze the latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.

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Rainfall Prediction in Lahore City using Data Mining Techniques

Cited by 36 publications

References 25 publications

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Seasonal and Periodic Autoregressive Time Series Models Used for Forecasting Analysis of Rainfall Data

Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

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