Library of model components for process simulation relevant to production activities, Prototype 1 versions

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An artificial neural network model for rainfall forecasting in Bangkok, Thailand

This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall

A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction

Time series data available in huge amounts can be used in decision-making. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.

Data Mining Techniques for Weather Prediction: A Review

Data mining is the computer assisted process of digging through and analysing enormous sets of data and then extracting the meaningful data. Data mining tools predicts behaviours and future trends, allowing businesses to make proactive decisions. It can answer questions that traditionally were very time consuming to resolve. Therefore they can be used to predict meteorological data that is weather prediction. Weather prediction is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help preparing for the best and the worst of the climate. Accurate Weather Prediction has been one of the most challenging problems around the world. Many weather predictions like rainfall prediction, thunderstorm prediction, predicting cloud conditions are major challenges for atmospheric research. This paper presents the review of Data Mining Techniques for Weather Prediction and studies the benefit of using it. The paper provides a survey of available literatures of some algorithms employed by different researchers to utilize various data mining techniques, for Weather Prediction. The work that has been done by various researchers in this field has been reviewed and compared in a tabular form. For weather prediction, decision tree and k-mean clustering proves to be good with higher prediction accuracy than other techniques of data mining

Implications of modeling seasonal differences in the extremal dependence of rainfall maxima

For modeling extreme rainfall, the widely used Brown–Resnick max-stable model extends the concept of the variogram to suit block maxima, allowing the explicit modeling of the extremal dependence shown by the spatial data. This extremal dependence stems from the geometrical characteristics of the observed rainfall, which is associated with different meteorological processes and is usually considered to be constant when designing the model for a study. However, depending on the region, this dependence can change throughout the year, as the prevailing meteorological conditions that drive the rainfall generation process change with the season. Therefore, this study analyzes the impact of the seasonal change in extremal dependence for the modeling of annual block maxima in the Berlin-Brandenburg region. For this study, two seasons were considered as proxies for different dominant meteorological conditions: summer for convective rainfall and winter for frontal/stratiform rainfall. Using maxima from both seasons, we compared the skill of a linear model with spatial covariates (that assumed spatial independence) with the skill of a Brown–Resnick max-stable model. This comparison showed a considerable difference between seasons, with the isotropic Brown–Resnick model showing considerable loss of skill for the winter maxima. We conclude that the assumptions commonly made when using the Brown–Resnick model are appropriate for modeling summer (i.e., convective) events, but further work should be done for modeling other types of precipitation regimes

Development of Intensity Duration Frequency Curve of the Lower Rio Grande Valley

Section 1: This chapter incorporates extensive information about the concept of Intensity-Duration-Frequency (IDF) curves, their historical development and usage, specific focus on the Lower Rio Grande Valley, and the importance of using a comprehensive 32-year data set from 1990-2022 to develop these curves. Section 2: This chapter provides detailed information about the rigorous comparison of the developed IDF curves with previous ones, where past IDF curves were developed using an empirical formula method. The research presented here employs the Gumbel distribution method, providing a fresh perspective and potentially more accurate predictions of future weather extremes. Section 3: This chapter outlines the process of developing hyetographs and the methodologies used to determine the threshold of hurricanes. It offers insights into how these hyetographs can be used for predicting hurricanes, underscoring the critical importance of this research in contributing to our understanding of extreme weather events, and offering potential strategies for future weather predictions and disaster preparedness

Introducing an effect of climate change into globals models of rain fade on telecommunications links

Rain attenuation limits the performance of microwave telecommunication links functioning above approximately 5 GHz. Recent studies have revealed that over the last twenty years the occurrence of rain, at intensities that cause outage on terrestrial links, has experienced a strongly increasing trend in the UK. Globally, the height of rain events has also been observed to increase, which may compound increasing trends in rain fade experienced by Earth-Space communication systems. These climatic changes are almost certainly having significant effect on the performance of existing radio systems, and need to be taken into consideration when planning future systems. The International Telecommunication Union – Radio Section (ITU-R), maintains a set of internationally accepted models for the engineering and regulation of radio systems globally. Although under constant revision, these models assume that atmospheric fading is stationary. This assumption is inherent in the way models are tested.In this project, a method is developed to estimate global trends in one of the most fundamental parameters to the ITU-R models: the one-minute rain rate exceeded for 0.01% of an average year. This method introduces climate change into the ITU-R model of this parameter: Rec. ITU-R P.837. The new model is tested using a method that does not make a stationary climate assumption. Salonen-Poiares Baptista distribution, which is the fundamental method for developing ITU-R Rec. P.837 has been tested using UK Environment Agency data, but no correlations was found between measured annual accumulations and distribution parameters. Nonetheless a link was found between mean annual total precipitations (MT) and rain exceeded at larger time percentages such as; 0.1% and 1%

Extreme Rainfall Events: Incorporating Temporal and Spatial Dependence to Improve Statistical Models

The proper design of protective measurements against floods related to heavy precipitation has long been a question of interest in many fields of study. A crucial component for such design is the analysis of extreme historical rainfall using Extreme Value Theory (EVT) methods, which provide information about the frequency and magnitude of possible future events. Characterizing an entire basin or geographical catchment requires the extension of univariate EVT methods to capture the spatial variability of the data. This extension requires that the similarity of the data for nearby stations be included in the model, resulting in more efficient use of the data. This dissertation focuses on using statistical models incorporating spatial dependence for modeling annual rainfall maxima. Additionally, we present ways of adapting the models to capture the dependence between rainfall of different time scales. These models are used in order to pursue two aims. The first aim is to improve our understanding of the mechanisms that lead to dependence on extreme rainfall. The second aim is to improve the resulting estimates when incorporating the dependence into the models. Two published studies make up the main findings of this dissertation. The models used in both studies involve the use of Brown-Resnick max-stable processes, allowing the models to explicitly account for the dependence on either the temporal or the spatial domain. These conditional models are compared for both cases to a model that ignores the dependence, allowing us to determine the impact of the dependence in both situations. Contributions to three other studies using the concept of dependence are also summarized. In the first study, we assess the impact of including the dependence between rainfall series of different aggregation durations when estimating Intensity-Duration-Frequency curves. This assessment was done in a case study for the Wupper catchment in Germany. This study found that including the dependence in the model had a positive effect on the prediction accuracy when focusing on rainfall with short durations (d <= 10h) and large probabilities of non-exceedance. Therefore, we recommend using max-stable processes when a study focuses on short-duration rainfall. In the second study, we investigate how the spatial dependence of extreme rainfall in Berlin-Brandenburg changes seasonally and how this change could impact the estimates from a max-stable model that includes this dependence. The seasonality was determined by estimating the parameters of a summer and winter semi-annual block maxima model. The results from this study showed that, for the summer maxima, the dependence structure was adequately captured by an isotropic Brown-Resnick model. On the contrary, the same model performed poorly for the winter maxima, suggesting that a change in the assumptions is needed when dealing with typical winter events, typically frontal or stratiform for this region. These results show that accounting for the meteorological properties of the rainfall-generating processes can provide useful information for the design of the models. Overall, our findings show that including meteorological knowledge in statistical models can improve their resulting estimations. In particular, we find that, under certain conditions, using statistical dependence to incorporate knowledge about the differences in temporal and spatial scales of rainfall-generating mechanisms can lead to a positive impact in the models.Die richtige Auslegung von Schutzmaßnahmen gegen Überschwemmungen im Zusammenhang mit Starkniederschlägen ist seit langem eine Frage, die in vielen Studienbereichen von Interesse ist. Eine entscheidende Komponente für eine solche Planung ist die Analyse extremer historischer Niederschläge mit Methoden der Extremwertstatistik, die Informationen über die Häufigkeit und das Ausmaß möglicher künftiger Ereignisse liefern. Die Charakterisierung eines ganzen Einzugsgebiets oder einer geografischen Einheit erfordert die Erweiterung der univariaten Extremwerstatistik-Methoden, um die räumliche Variabilität der Daten zu erfassen. Diese Erweiterung erfordert, dass die Ähnlichkeit der Daten für nahe gelegene Stationen in das Modell einbezogen wird, was zu einer effizienteren Nutzung der Daten führt. Diese Dissertation konzentriert sich auf die Verwendung statistischer Modelle, die die räumliche Abhängigkeit bei der Modellierung von jährlichen Niederschlagsmaxima berücksichtigen. Darüber hinaus werden Möglichkeiten zur Anpassung der Modelle vorgestellt, um die Abhängigkeit zwischen Niederschlägen auf verschiedenen Zeitskalen zu erfassen. Diese Modelle werden zur Verfolgung zweier Ziele eingesetzt. Das erste Ziel besteht darin, unser Verständnis der Mechanismen zu verbessern, die zur Abhängigkeit von extremen Niederschlägen führen. Das zweite Ziel besteht darin, die resultierenden Schätzungen zu verbessern, wenn die Abhängigkeit in die Modelle einbezogen wird. Zwei veröffentlichte Studien bilden die wichtigsten Ergebnisse dieser Dissertation. Die in beiden Studien verwendeten Modelle basieren auf max-stabilen Brown-Resnick-Prozessen, die es den Modellen ermöglichen, die Abhängigkeit entweder auf der zeitlichen oder auf der räumlichen Ebene ausdrücklich zu berücksichtigen. Diese bedingten Modelle werden für beide Fälle mit einem Modell verglichen, das die Abhängigkeit ignoriert, so dass wir die Auswirkungen der Abhängigkeit in beiden Situationen bestimmen können. Es werden auch Beiträge zu drei anderen Studien zusammengefasst, die das Konzept der Abhängigkeit verwenden. In der ersten Studie bewerten wir die Auswirkungen der Einbeziehung der Abhängigkeit zwischen Niederschlagsreihen unterschiedlicher Aggregationsdauern bei der Schätzung von Intensitäts-Dauer-Frequenz-Kurven. Diese Bewertung wurde in einer Fallstudie für das Einzugsgebiet der Wupper in Deutschland durchgeführt. Diese Studie ergab, dass sich die Einbeziehung der Abhängigkeit in das Modell positiv auf die Vorhersagegenauigkeit auswirkt, wenn man sich auf Niederschläge mit kurzen Dauern (d <= 10 h) und großer Nichtüberschreitungwahrscheinlichkeit konzentriert. Daher empfehlen wir die Verwendung von max-stabilen Prozessen, wenn sich eine Studie auf Regenfälle von kurzer Dauer konzentriert. In der zweiten Studie untersuchen wir, wie sich die räumliche Abhängigkeit von Extremniederschlägen in Berlin-Brandenburg saisonal verändert und wie sich diese Veränderung auf die Schätzungen eines max-stabilen Modells auswirken könnte, das diese Abhängigkeit berücksichtigt. Die Saisonalität wurde durch die Schätzung der Parameter eines halbjährlichen Sommer- und Winter-Blockmaxima-Modells bestimmt. Die Ergebnisse dieser Studie zeigten, dass die Abhängigkeitsstruktur für die Sommermaxima durch ein isotropes Brown-Resnick-Modell angemessen erfasst wurde. Im Gegensatz dazu zeigte dasselbe Modell eine schlechte Leistung für die Wintermaxima, was darauf hindeutet, dass eine Änderung der Annahmen erforderlich ist, wenn es um typische Winterereignisse geht, die in dieser Region typischerweise frontal oder stratiförmig sind. Diese Ergebnisse zeigen, dass die Berücksichtigung der meteorologischen Eigenschaften der Niederschlagsprozesse nützliche Informationen für die Gestaltung der Modelle liefern kann. Insgesamt zeigen unsere Ergebnisse, dass die Einbeziehung von meteorologischem Wissen in statistische Modelle die daraus resultierenden Schätzungen verbessern kann. Insbesondere stellen wir fest, dass unter bestimmten Bedingungen die Nutzung der statistischen Abhängigkeit zur Einbeziehung von Wissen über die Unterschiede in den zeitlichen und räumlichen Skalen der regenerzeugenden Mechanismen zu einer positiven Wirkung in den Modellen führen kann