Rainfall prediction using Artificial Neural Network in the South Pacific region

Chand, Ashneel; Nand, Ravneil

doi:10.1109/csde48274.2019.9162367

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study investigated different activation functions of the MLP model for pan evaporation estimation in a semi-arid region. Also, it is recommended to use different activation functions of the MLP model for hydrological modeling by MLP model, such as actual evaporation [57,62], rainfall [63], runoff [64], solar radiation [65], snow cov-er area [66], soil temperature [67], soil pore-water pressure [68] simulation. In some studies, the performance of different learning algorithms for training the MLP model was evaluated [63,68,69] for modeling different hydrological variables.…”

Section: Discussionmentioning

confidence: 99%

“…Also, it is recommended to use different activation functions of the MLP model for hydrological modeling by MLP model, such as actual evaporation [57,62], rainfall [63], runoff [64], solar radiation [65], snow cov-er area [66], soil temperature [67], soil pore-water pressure [68] simulation. In some studies, the performance of different learning algorithms for training the MLP model was evaluated [63,68,69] for modeling different hydrological variables. For example, Mustafa et al [69] used different learning algorithms to improve the modeling of soil pore water pressure responses to rainfall.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Aghelpour

Bagheri-Khalili

Varshavian

et al. 2022

Water

View full text Add to dashboard Cite

Evaporation is one of the main components of the hydrological cycle, and its estimation is crucial and important for water resources management issues. Access to a reliable estimator tool for evaporation simulation is important in arid and semi-arid areas such as Iran, which lose more than 70% of their received precipitation by evaporation. Current research employs the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms for training the Multilayer Perceptron (MLP) model (as MLP-BR and MLP-SCG) and comparing their performance with the Levenberg–Marquardt (LM) algorithm (as MLP-LM). For this purpose, 16 meteorological variables were used on a daily scale; including temperature (5 variables), air pressure (4 variables), and relative humidity (6 variables) as input data sets, and pan evaporation as the target variable of the MLP model. The surveys were conducted during the period of 2006–2021 in Fars Province in Iran, which is a semi-arid region and has many natural lakes. Various combinations of input-target pairs were tested by several learning algorithms, resulting in seven input scenarios: (1) temperature-based (T), (2) pressure-based (F), (3) humidity-based (RH), (4) temperature–pressure-based (T-F), (5) temperature–humidity-based (T-RH), (6) pressure–humidity-based (F-RH) and (7) temperature–pressure–humidity-based (T-F-RH). The results indicated the relative superiority of the three-component scenario of T-F-RH, and a considerable weakness in the single-component scenario of RH compared with others. The best performance with a root mean square error (RMSE) equal to 1.629 and 1.742 mm per day and a Wilmott Index (WI) equal to 0.957 and 0.949 (respectively for validation and test periods) belonged to the MLP-BR model. Additionally, the amount of R2 (greater than 84%), Nash-Sutcliff efficiency (greater than 0.8) and normalized RMSE (less than 0.1) all indicate the reliability of the estimates provided for the daily pan evaporation. In the comparison between the studied training algorithms, two algorithms, BR and SCG, in most cases, showed better performance than the powerful and common LM algorithm. The obtained results suggest that future researchers in this field consider BR and SCG training algorithms for the supervised training of MLP for the numerical estimation of pan evaporation by the MLP model.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Aghelpour

Bagheri-Khalili

Varshavian

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…The real time data obtained were normalised using equation (1). The idea behind this was to minimise the data redundancy and improves data integrity [31].…”

Section: A Data Collection and Processingmentioning

confidence: 99%

Data Mining Students' performance in a Higher Learning Environment

Nand

Chand

Reddy

2021

2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)

Self Cite

View full text Add to dashboard Cite

Student performance in higher education has become one of the most widely studied area. While modelling students' performance, data plays a pivotal role in forecasting their performance and this is where the data mining applications are now becoming widely used. There are various factors which determine the student performance. In this study, eight attributes are used as inputs which are considered most influential in determining students' performance in the Pacific. Statistical analysis is done to find out which attribute has the highest influence to student performance. In this research, different algorithms are utilized for building the classification model, each of them using various classification techniques. The classification techniques used are Artificial Neural Network, Decision Tree, Decision Table, and Naïve Bayes. The dataset of 651 records used in this research is an imbalanced set, which is later transformed to balance set through under sampling. Neural Network is one of the classification techniques that has performed well on both, imbalanced and balanced datasets with the highest prediction accuracy of 96.8%. The analysis further shows that internal assessment has weak positive relationship with student performance while demographic data has no significant relationship.

show abstract

“…Date cleaning has been the first step before it has been normalized. The real time data obtained was normalized as in [18].…”

Section: B Data Collection and Processingmentioning

confidence: 99%

Modelling Student’s Performance Using Data Mining Techniques in a Higher Learning Environment in the Pacific

Nand

Chand

2020

International Journal of Neural Networks and Advanced Applications

Self Cite

View full text Add to dashboard Cite

The students’ performance in higher education has become one of the most widely studied area. Modelling student performance play a pivotal role in forecasting students’ performance where the data mining applications are now becoming most widely used techniques in this study. There are various factors, which determine the student performance. Eight attributes are used as input, which is considered most influential in determining students’ performance in the Pacific. Statistical analysis is done to see which attribute has the highest influence to student performance. In this research, different algorithms are utilized for building the classification model, each of them using various classification techniques. Some of classification techniques used are Artificial Neural Network, Decision Tree, Decision Table, and Naïve Bayes. The WEKA explorer application and R software are used for correlation test between different variables. The dataset used in this research is an imbalanced set, which is later transformed to balance set through under sampling. Neural Network is one of the classification techniques that has done well on both, imbalanced and balanced dataset. Another technique which has done well is Decision tree. Statistical analysis shows that internal assessment has weak positive relationship with student performance while demographic data is not. Further observations are reported in this research in relation to two types of datasets with application to different classification techniques

show abstract

Rainfall prediction using Artificial Neural Network in the South Pacific region

Abstract: hc nYfc) LbXciVhYX`m' bYifU` bYhk cf_ dfcjYg hc VY acgh Uddfcdf]UhY hYW\b]eiY Zc f ZcfYWUgh]b[ jUf]cig kYUh\Yf d\YbcaYbU giW\ Ug fU]bZU``) A h WUb VY Zi fh\ Y f U``iXYX h\Uh :UmYg]Ub IY[i`Uf]nUh]cb hYbXg hc []jY `ckYf EJ= jU`iYg WcadUfY hc h\Y ch\Yf hkc hfU]b]b[ U`[cf]h\ag)

Cited by 5 publications

References 12 publications

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Data Mining Students' performance in a Higher Learning Environment

Modelling Student’s Performance Using Data Mining Techniques in a Higher Learning Environment in the Pacific

Contact Info

Product

Resources

About