PROBABILITY DISTRIBUTION FUNCTIONS IN RAINFALL ANALYSIS Sugandh Pratap Singh (2016CEW2427) INTRODUCTION Water is the source of all life on Earth. The total amount of water present on the earth is fixed and does not change. Rainfall intensities of various frequencies and durations are the basic input in hydrologic design. Precipitation frequency analysis is used to estimate rainfall depth at a point for a specified exceedence probability and duration. Rainfall frequency analysis is usually based on annual maximum series at a site (at-site analysis) or from several sites (regional analysis).
According to VASAT (2013), rainfall is occurred when the moisture at the atmosphere condenses into water drops. Rainfall will fall towards the grounds when the water drops is too heavy to stay aloft. Hubbart (2011) described hydrologic cycle which also known as water cycle as a conceptual model that describes the storage and movement of water between the biosphere, atmosphere, lithosphere and hydrosphere. Figure 2.1 illustrates the hydrological cycle. Three types of rainfall are convectional rainfall, cyclonic rainfall and orographic rainfall (Rakhecha and Singh, 2010; Das and Saikia, 2009).
Introduction: In this task I will be researching the effect that acid rain has on the rate of plant growth. Acid rain is any type of precipitation with a high pH, with high levels of nitric acids. The reason why I had chosen this topic was because acid rain seems to have a great effect on the effect of plant growth, and plants play a very important role in our ecosystem. Acid rain is a major problem in our environment when we are not able to neutralize the acidity. Research Questions: What effect does acid rain have on the growth rate of plants in the wetland ecosystem?
2.2.3 Forecasting Forecasting refers to calculate or to determine the probability of the future demand. In most forecasting methods the assumption is that the past demand pattern or behavior will continue in the same pattern in future (Frank et al., 2003). Forecasting is an essential part of most private and public organizations. Quantities of the items that need to be forecast and their correctness can have a direct impact on the performance of the company. Forecast accuracy can have many positive results on the overall performance of the organization, for instance; low inventory level, less manufacturing cost, higher customer satisfaction level and low level of obsolescence.
Rationale: Regardless of whether you realize or not, we are surrounded by probability. Consistently, we use probability to plan around the weather. Meteorologists can't predict precisely what the weather will be, so they use devices and instruments to decide the probability that it will rain, snow or hail. When the doctor gives us chances to survive, its probability. According to Eliezer S. Yudkowsky, “Reality dishes out experiences using probability, not plausibility”.
Once an all-human endeavor based mainly upon changes in barometric pressure, current weather conditions, and sky condition, weather forecasting now relies on computer based models that take many atmospheric factors into account. However, the chaotic nature of the atmosphere and incomplete understanding of the processes mean that forecasts become less accurate as the range of the forecast increases. There are a variety of end uses to weather forecasts. Weather warnings are important forecasts because they are used to protect life and property. Numerical weather prediction models are computer simulations of the atmosphere.
The use of pesticides can be reduced with the help of forecasting of diseases and pest infection. Infection rate and disease severity are highly dependent on environmental parameters like temperature, humidity, leaf wetness duration, rainfall, etc. Using correlation of these parameters with the infection rate, a mathematical prediction model can be devised to estimate the future value of infection. It predicts risk or no risk for the particular infection to occur on that particular crop . Advance information about severity of risk help to alert the farmers to manage the quality and quantity of spray of pesticides for particular pest and disease.
Performance of Rainwater Harvesting System Based on Roof Catchment Area and Storage Tank Capacity Imroatul C. Juliana1, M. Syahril Badri Kusuma2,4, M. Cahyono2,4, Hadi Kardhana2,5, Widjaja Martokusumo3 1Ph.D Student, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Indonesia 2Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Indonesia 3School of Architecture, Planning, and Development Policy, Bandung Institute of Technology, Indonesia 4Water Resources Development Center, Bandung Institute of Technology, Indonesia 5Center for Research on Infrastructure and Regional, Bandung Institute of Technology, Indonesia Abstract. Increasing population growth has created problems in water resources. Natural water resources become progressively more expensive and difficult to develop. In addition, it is also becoming increasingly polluted and difficult to obtain, largely due to human activities. Many countries shown a resurgent interest in the use of rainwater harvesting (RWH) technique to overcome these problems.
INTRODUCTION Rainfall simulation has been used with much success throughout the last 75 years to conduct research on infiltration, surface water runoff and soil erosion. Young and Burwell(1972) pointed out the advantage of using simulated rain as opposed to natural rainfall is desirable as it represented natural conditions at a given place, data acquisition is very slow and the spatial and temporal distribution of rainfall intensity, duration and kinetic energy can be controlled. A rainfall simulator is relatively easy to operate and transport while maintaining critical intensity, distribution, energy characteristics and time response of natural rainfall. It is developed with objective to simulate rainfall which is PC controlled hardware
In this paper, Rainfall, soil data and climate dataset are used to predict the crop production.These types of datasets are preprocessed that removes the unwanted and null data in the dataset. Feature extraction method is used to extracts a subset of new features from the datasets through functional mapping to maintain the information .In feature selection, genetic algorithm is used to select optimal features. Thegenetic algorithm is provided the opportunity to discover the optimum solution .The enhanced ANFIS classifier is used in this work that contain improved C4.5 classifier in hidden layer that generate the rules to predict the yield. Section II reviews the literature on Agricultural production. The proposed method is demonstrated in Section III.