Possibility Of Rain Research Articles

Comparative Analysis of K-Means and K-Medoids Clustering Methods on Weather Data of Denpasar City

By applying data mining methods, particularly clustering techniques, the weather data of Denpasar city can be grouped based on similar characteristics. This provides deep insight into weather patterns, useful for more optimised travel planning. This research positively impacts tourism, helping stakeholders understand weather patterns in more detail. Furthermore, in-depth knowledge of weather conditions improves preparedness for potential global climate change. The clustering results can be visualised in a three-dimensional cartesian diagram, providing a clear picture of the various weather conditions using attributes such as temperature, precipitation, and humidity. Through Kaggle's Denpasar Weather Data dataset, with 264,924 data and 32 columns, this study illustrates that cloudy weather dominates in the K-Means and K-Medoids clustering process on rain data within one hour. At three hours, K-Means shows the dominance of cloudy weather and the possibility of rain, while K-means dominates all clusters. At six hours, K-Means dominate in sunny and rainy weather, while K-Medoids dominate evenly in all clusters.

The Use of Weather Website Data for Construction Project Decision-Making in The Short Term

In most work sites, it is a priority to keep the work going well and to avoid unforeseen incidents. Fluctuations in weather conditions are one of the factors affecting the continuity of work in construction projects. Indeed, for example, the temperature is important in concrete and asphalt works, and wind speed is important in lifting and high construction works. Therefore, taking the appropriate decision, starting and completing the work, is very important to maintain the quality of the project. This research aims to demonstrate the reliability of short-term decision-making through data taken from the weather site five days before the time to work. The data was collected for a month, five days before the intended day and on the same day, day and night, for different weather factors by weather location such as temperature, humidity, possibility of rain, Uv index, wind speed. By analyzing the data, it was found that there was little difference in those predictors of all the factors recorded. To conclude at the end of the study that it is possible to rely on the decision-making on the weather location in small and medium projects, but in large and sensitive projects, they need to rely on more accurate data than relying on weather location data.

The Intentionality in the short Meccan Surahs: An Applied Study

The studies conducted about Qur'an are still trending and modern linguistic studies are not new to the Qur'an, even if they come in different names. The verses of the Qur'an include general, specific and partial purposes, and the researchers should pay attention to these studies and explain their intent through interpreting and analyzing the verses. Any linguistic or non-linguistic examination must contain the subconscious meaning behind the virtual terms, as the gathering of clouds indicates the possibility of rain, but the storm may come and dispersal of these clouds and rain does not fall, and so is the meaning of the intent and see something apparent but in the subconscious, it means something else and it is implicitly understood by discourse analysis and digging for the deep meaning. The ancient Arab scholars had a great role in this field, but as it is mentioned in the research, it was dispersed and scattered between the books and literature. The semantics (المقصدية), is one aspect of these dispersed topics and it was not a separate subject as it was in the western studies. The fence, which lies beyond the meaning, and each word in it indicates certain aspects, and each researcher understands what is meant according to his psychological state or what suits his thinking, and this is the core of the intent. This research examines The Fence of Makkah.

Global Cue Inconsistency Diminishes Learning of Cue Validity.

In daily life, we make decisions that are associated with probabilistic outcomes (e.g., the chance of rain today). People search for and utilize information that validly predicts an outcome (i.e., we look for dark clouds to indicate the possibility of rain). In the current study (N = 107), we present a two-stage learning task that examines how participants learn and utilize predictive information within a probabilistic learning environment. In the first stage, participants select one of three cues that gives predictive information about the outcome of the second stage. Participants then use this information to predict the outcome in stage two, for which they receive feedback. Critically, only one of the three cues in stage one gives valid predictive information about the outcome in stage two. Participants must differentiate the valid from non-valid cues and select this cue on subsequent trials in order to inform their prediction of the outcome in stage two. The validity of this predictive information, however, is reinforced with varying levels of probabilistic feedback (i.e., 75, 85, 95, 100%). A second manipulation involved changing the consistency of the predictive information in stage one and the outcome in stage two. The results show that participants, with higher levels of probabilistic feedback, learned to utilize the valid cue. In inconsistent task conditions, however, participants were significantly less successful in utilizing higher validity cues. We interpret this result as implying that learning in probabilistic categorization is based on developing a representation of the task that allows for goal-directed action.

A method for classification accuracy evaluation for a precipitation‐oriented classifier

A method of classification accuracy evaluation for a cloud and precipitation classifier applied to geostationary meteorological satellite data is presented. The method has been developed to evaluate the accuracy of a rather precise classification algorithm. The algorithm produces nine classes, four of which involve precipitation. The classes are: (1) clear or insignificant cloud, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light–moderate precipitation, (8) middle–high cloud with moderate–heavy precipitation, (9) heavy thunderstorm. The evaluation classifier has been tested for its accuracy (ground truth) using comparison between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification‐produced values. The classifier used has proved to be very reliable for classifying major cloud types and precipitation, tested during the synoptic situation of depression systems approaching the south Balkan Peninsula from the west. In that synoptic situation, different intensities of rainfall as well as heavy thunderstorm were present, and the results are very satisfactory. The method can be used to evaluate classification results produced by algorithms applied to meteorological satellite data, classifying precipitation areas as well as the heaviness of precipitation.

Cloud and precipitation classification for a depression system approaching the south Balkan Peninsula. A case study of 26 March 1998

A method is presented that has been developed for classifying cloud and precipitation for a specific synoptic situation affecting south-east Europe during winter and spring: a depression system that gives rise to a heavy rainfall weather situation. The classification algorithm used for classifying clouds and precipitation is based on a combination of spectral channels and the multispectral box classification technique using visible and infrared Meteosat data. In order to increase the accuracy of the classification algorithm, the appropriate cloud types are selected according to the specific synoptic weather situation. The classification algorithm produces nine classes, four of which involve precipitation. These are: (1) clear or insignificant clouds, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light-moderate precipitation, (8) middle-high cloud with moderate-heavy precipitation, (9) heavy thunderstorm. The classifier has been tested for its accuracy (ground truth) using comparisons between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification produced values. The case study of 26 March 1998 is presented. The classifier used has proved to be very reliable for classifying major cloud types and precipitation during the synoptic situation of depression systems approaching the south Balkan peninsula from the west. It has been tested for different intensities of rainfall as well as for heavy thunderstorms and the results are very satisfactory. The results produced can be used to support the forecaster's daily work. The need for the extraction of such classification products lies in the fact that detailed weather information is demanded for regions in Greece devoid of surface observations, such as the Aegean Sea, the Ionian Sea, the South Cretan Sea and remote mountainous areas. That information has been found to be useful, especially in cases where significant weather systems are approaching Greece from the west or south-west, where surface observations over the sea are not available and an early knowledge of the heaviness of precipitation is needed, even before that weather system is in range of any land-based weather radar. Moreover, the single use of isolated channels (i.e. infrared or visible) could involve the danger of errors in the interpretation of the satellite image.