Knowledge Discovery in Geographical Sciences—A Systematic Survey of Various Machine Learning Algorithms for Rainfall Prediction

Fayaz, Sheikh Amir; Zaman, Majid; Butt, Muheet Ahmed

doi:10.1007/978-981-16-2597-8_51

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…Smoothing is also conducted after pruning on the created Model Tree, which is used to prevent the harsh discontinuities of the sub trees. Flattening the sharp nodes of nearby models is a typical way to enhance prediction accuracy [10,11].…”

Section: M5 Model Treementioning

confidence: 99%

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Fayaz¹,

Zaman²,

Kaul³

et al. 2022

RIA

Self Cite

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When using machine learning to predict a class with a continuous numeric value, there are several issues. Only a few machine-learning approaches are capable of doing so, but it remains one of the most difficult jobs to do. In this paper, we show how to use the M5 Model Tree, an approach that can handle continuous numeric data. This method is a stepwise procedure that employs linear functions at the leaf nodes of any created decision tree inducer (such as CART). These M5 model trees provide basic practical formulas such as standard deviation (SD), standard deviation reduction (SDR), cost-complexity pruning (CCP), and so on, which may be simply applied to different benchmark data by another user. This study examines the M5 Model Tree algorithm's capabilities for analysing rainfall data in the Kashmir portion of India's Union Territory of Jammu & Kashmir. One of the best suited models was the M5 model tree, which was built using (70–30) percent training and test ratios, respectively, and predicted an RMSE of 2.593, an MAE of 1.68, and a correlation coefficient (R2) of 0.478. Furthermore, M5 model trees produce models with a minimal number of trails, requiring less computing effort and making them more practical to use.

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Section: M5 Model Treementioning

confidence: 99%

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Fayaz¹,

Zaman²,

Kaul³

et al. 2022

RIA

Self Cite

View full text Add to dashboard Cite

show abstract

“…The execution of this directive, however, is largely dependent on human skill [6]. The FL method is based on the decomposition of a real variable's range of variation into linguistic variables and assigning a membership function to each variable [1,3,4,11,16,[20][21][22][23][24][25][26][27][28][29]. The rules derived from the human operator's competence are conveyed in language form.…”

Section: Fuzzy Control Algorithm[6]mentioning

confidence: 99%

“…The applied rules ensure that the relationship between the FL controller's inputs and outputs are listed in Table 2. For constant growth systems, the symmetric rule basis is commonly utilized [1,3,4,11,15,[25][26][27][28][29][30][31][32][34][35][36][37].…”

Section: Fuzzy Control Algorithm[6]mentioning

confidence: 99%

“…As a result of this progress, complex PV grid topologies have begun to evolve to boost power, efficiency, and reliability. Furthermore, grid-connected PV systems (which do not require batteries) are well-known for being less expensive than stand-alone PV systems (which do require batteries) [3]. Despite significant advancements in the field of PV systems, there are still issues with PV grids such as voltage regulation, power quality, and grid problems.…”

Section: Introductionmentioning

confidence: 99%

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Design and Simulation of Grid-Connected Photovoltaic System’s Performance Analysis with Optimal Control of Maximum Power Point Tracking Based on Artificial Intelligence

Faouzi

Chaouche²

2022

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The research presented in this paper is part of a more significant effort to improve the dynamic and static performance of power generation systems using solar panels under specific climatic conditions. The solar panel can produce the greatest power only at the specified voltage and electric current levels. Environmental variables, such as random atmospheric oscillations, have a significant effect on the performance of a solar system connected to the network. Irradiation and ambient temperature are the two inputs to a PV (photovoltaic) system. Because solar radiation varies in nature, the PV system efficiency is low. To improve the efficiency of a solar PV system, several maximum power point tracking (MPPT) approaches are used. The purpose of this paper is to improve the performance of DC/DC chopper controllers and PV inverters in the face of abrupt climate change. To that end, the primary goal of this paper is to compare the following maximum power point search (MPPT) algorithms: the incremental conductance (IC) algorithm, the fuzzy logic (FL) algorithm, VSI controller, and the particle swarm optimization (PSO) strategy. These algorithms were evaluated in terms of efficiency, stability, and speed. A 100 kW PV system is design using MATLAB software 2021a version.

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“…Artificial Intelligence (AI) is rapidly transforming our world. With time it has spread into many business areas [1][2][3] and is originating in medical field too with the increase in the complication and evolution of data in biological sciences, AI is gradually being applied within the field. The AI technologies such as machine learning (ML) and deep learning (DL) [4][5][6][7] have played a foremost role in the detection and prediction of diseases [8,9] either by means of the disease symptom datasets [10,11] or medical image datasets, which have helped the doctors in a positive way.…”

Section: Introductionmentioning

confidence: 99%

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

Altaf

Butt²,

Zaman³

2022

Self Cite

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To predict and detect various diseases, machine learning techniques are increasingly being used in the field of medical science. This study puts forward a bagging meta-estimator and feed forward neural network based voting ensemble with mean decrease in impurity feature selection to classify the disease datasets. The work was carried out using the Jupyter notebook data analysis tool, and Python 3 is used as a programming language. In this study, two chronic disease datasets - Indian Liver Patient dataset and the PIMA Indians diabetes dataset are used for building and testing the proposed model. The datasets are split into training and testing data in the ratio of 70:30. The experimental results illustrate that our proposed voting ensemble has an improved performance compared to the individual base learners. We also compared the accuracy of the model before and after the application of feature reduction technique. The results revealed that the accuracy increased with the removal of unimportant features. By using the proposed ensemble model, the average MSE, bias and variance were calculated as 0.311, 0.217 and 0.094 respectively for ILPD dataset. Similarly for PIMA dataset, the average MSE, bias and variance were calculated as 0.233, 0.186 and 0.047 respectively. These statistical parameters record a low score for ensemble classifier as compared to the individual constituent classifiers.

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Knowledge Discovery in Geographical Sciences—A Systematic Survey of Various Machine Learning Algorithms for Rainfall Prediction

Cited by 12 publications

References 27 publications

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Design and Simulation of Grid-Connected Photovoltaic System’s Performance Analysis with Optimal Control of Maximum Power Point Tracking Based on Artificial Intelligence

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

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