Academic literature on the topic 'Accurate rainfall prediction'
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Journal articles on the topic "Accurate rainfall prediction"
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Waghmare, Dr Vivek. "Machine Learning Technique for Rainfall Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 594–600. http://dx.doi.org/10.22214/ijraset.2021.35032.
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Rain prediction is one of the most challenging and uncertain tasks that has a profound effect on human society. Timely and accurate forecasting can help significantly reduce population and financial losses. This study presents a collection of tests involving the use of conventional machine learning techniques to create rainfall prediction models depending on the weather information of the area. This Comparative research was conducted focusing on three aspects: modeling inputs, modeling methods, and prioritization techniques. The results provide a comparison of the various test metrics for these machine learning methods and their reliability estimates in rain by analyzing weather data. This study seeks a unique and effective machine learning system for predicting rainfall. The study experimented with different parameters of the rainfall from various regions in order to assess the efficiency and durability of the model. The machine learning model is focused on this study. Rainfall patterns in this study are collected, trained and tested for achievement of sustainable outcomes using machine learning models. The monthly rainfall predictions obtained after training and testing are then compared to real data to ensure the accuracy of the model The results of this study indicate that the model has been successful in it predicting monthly rain data and specific parameters.
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Wakure, Govind, Rahul Lotlikar, Pushant Mangilipelli, and Saif Khan. "Rainfall Prediction Model Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 939–44. http://dx.doi.org/10.22214/ijraset.2022.42244.
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Abstract: Predicting rainfall is one of the most difficult aspects of weather forecasting. Accurate and timely rainfall forecasting can be extremely useful in preparing for ongoing building projects, transportation activities, agricultural jobs, aviation operations, and flood situations, among other things. By finding hidden patterns from available elements of past meteorological data, machine learning techniques may accurately predict rainfall. This study adds to the body of knowledge by giving a comprehensive examination and assessment of the most recent Machine Learning algorithms for rainfall prediction. This study looked at publications that were published between 2013 and 2017 and were found in reputable internet search libraries. This study will aid academics in analysing recent rainfall prediction work with a focus on data mining approaches, as well as providing a baseline for future directions and comparisons. Keywords: Rainfall prediction, Rainfall, Machine learning techniques.
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Taiwo Amoo, Oseni, and Bloodless Dzwairo. "Trend analysis and artificial neural networks forecasting for rainfall prediction." Environmental Economics 7, no. 4 (December 21, 2016): 149–60. http://dx.doi.org/10.21511/ee.07(4-1).2016.07.
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The growing severe damage and sustained nature of the recent drought in some parts of the globe have resulted in the need to conduct studies relating to rainfall forecasting and effective integrated water resources management. This research examines and analyzes the use and ability of artificial neural networks (ANNs) in forecasting future trends of rainfall indices for Mkomazi Basin, South Africa. The approach used the theory of back propagation neural networks, after which a model was developed to predict the future rainfall occurrence using an environmental fed variable for closing up. Once this was accomplished, the ANNs’ accuracy was compared against a traditional forecasting method called multiple linear regression. The probability of an accurate forecast was calculated using conditional probabilities for the two models. Given the accuracy of the forecast, the benefits of the ANNs as a vital tool for decision makers in mitigating drought related concerns was enunciated. Keywords: artificial neural networks, drought, rainfall case forecast, multiple linear regression. JEL Classification: C53, C45
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Mohd, Razeef, Muheet Ahmed Butt, and Majid Zaman Baba. "Comparative Study of Rainfall Prediction Modeling Techniques (A Case Study on Srinagar, J&K, India)." Asian Journal of Computer Science and Technology 7, no. 3 (November 5, 2018): 13–19. http://dx.doi.org/10.51983/ajcst-2018.7.3.1901.
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Prediction of rainfall is one of the most essential and demanding tasks for the weather forecasters since ages. Rainfall prediction plays an important role in the field of farming and industries. Precise rainfall prediction is vital for detecting the heavy rainfall and to provide the information of warnings regarding the natural calamities. Rainfall prediction involves recording the various parameters of weather like wind direction, wind speed, humidity, rainfall, temperature etc. From last few decades, it has been seen that data mining techniques have achieved good performance and accuracy in weather prediction than traditional statistical methods. This research work aims to compare the performance of few data mining algorithms for predicting rainfall using historical weather data of Srinagar, India, which is collected from http://www.wundergrounds.com website. From the collected weather data which comprises of 9 attributes, only 5 attributes which are most relevant to rainfall prediction are considered. Data mining process model is followed to obtain accurate and correct prediction results. In this paper, various data mining algorithms were explored which include decision tree based J48, Random forest, Naive Bayes, Bayes Net, Logistic Regression, IBk, PART and bagging. The experimental results show that J48 algorithm has good level of accuracy than other algorithms.
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Manandhar, Shilpa, Soumyabrata Dev, Yee Hui Lee, Yu Song Meng, and Stefan Winkler. "A Data-Driven Approach for Accurate Rainfall Prediction." IEEE Transactions on Geoscience and Remote Sensing 57, no. 11 (November 2019): 9323–31. http://dx.doi.org/10.1109/tgrs.2019.2926110.
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Subhan Panji Cipta. "PENERAPAN ALGORITMA EVOLVING NEURAL NETWORK UNTUK PREDIKSI CURAH HUJAN." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 1, no. 1 (January 13, 2016): 1–8. http://dx.doi.org/10.20527/jtiulm.v1i1.2.
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Weather and climate information have contributed as one consideration for decision makers. This arises because the information the weather / climate has economic value in a variety of activities , ranging from agriculture to flood control . From the data obtained implied that the current rainfall prediction not so accurate . Forecasts are often given to the public on a regular basis is the weather forecast , not the amount of rainfall. This study uses an algorithm Evolving Neural Network (ENN) as an approach to predict the rainfall , the data processing and calculations will use MatLab 2009b . The parameters used in this study is time , rainfall , humidity and temperature. The results also compared with the test results and predictions BPNN BMKG. From the results of research conducted from early stage to test and measurement , the application of this ENN has a rainfall prediction with accuracy better than the BPNN and prediction algorithms BMKG.
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Et.al, S. Sakthivel. "Effective Procedure to Predict Rainfall Conditions using Hybrid Machine Learning Strategies." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 209–16. http://dx.doi.org/10.17762/turcomat.v12i6.1291.
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In the present information technology stream supports many natural disaster prediction schemes to save several people from disaster scenarios. In such case, rainfall prediction and analysis is the most important concern to take care as well as the prediction of high rainfall saves many individual's life and their assets. This kind of rainfall prediction schemes provides a facilitation to take respective precautions to avoid huge damages further. The rainfall predictions are categorized into two different variants such as Limited Period Rainfall Prediction and the long period Continuous Rainfall Prediction. Several past analysis and literatures provide accurate predictions for limited period rainfall but the major problem is to identify or predict the continuous long period rainfall. This kind of drawbacks leads many researchers to work on this domain and predict the rainfall status exactly for both limited period as well as long period continues rainfall. In this paper, a new hybrid machine learning strategy is implemented to predict the rainfall status exactly, in which the proposed methodology is named as Intense Neural Network Mining (INNM). This proposed approach of INNM analyze the rainfall prediction scenario based on two different machine learning logics such as Back Propagation Neural Network and the Rapid Miner. The general machine learning algorithms train the machine with respect to the dataset features and predict the result based on testing input. In this approach two different variants of machine learning principles are utilized to classify the resulting nature with better accuracy levels and cross-validations are providing best probabilistic results in outcome. And these two logics are integrated together to produce a new hybrid machine learning strategy to predict the rainfall status exactly and save human life against disasters. In this paper, a novel dataset is utilized from Regional Meteorological Centre Chennai to predict the rainfall summary in clear manner and the summarization of specific dataset is described on further sections. The proposed approach of INNM assures the resulting accuracy levels around 96.5% in prediction with lowest error ratio of 0.04% and the resulting portion of this paper provides a proper proof of this outcome in graphical manner.
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Skarlatos, Kyriakos, Eleni S. Bekri, Dimitrios Georgakellos, Polychronis Economou, and Sotirios Bersimis. "Projecting Annual Rainfall Timeseries Using Machine Learning Techniques." Energies 16, no. 3 (February 2, 2023): 1459. http://dx.doi.org/10.3390/en16031459.
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Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.
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Ardi, Yuan, Syahril Effendi, and Erna Budhiarti Nababan. "Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction." Randwick International of Social Science Journal 2, no. 2 (April 30, 2021): 176–92. http://dx.doi.org/10.47175/rissj.v2i2.240.
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Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules
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Billah, Mustain, Md Nasim Adnan, Mostafijur Rahman Akhond, Romana Rahman Ema, Md Alam Hossain, and Syed Md. Galib. "Rainfall prediction system for Bangladesh using long short-term memory." Open Computer Science 12, no. 1 (January 1, 2022): 323–31. http://dx.doi.org/10.1515/comp-2022-0254.
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Abstract Rainfall prediction is a challenging task and has extreme significance in weather forecasting. Accurate rainfall prediction can play a great role in agricultural, aviation, natural phenomenon, flood, construction, transport, etc. Weather or climate is assumed to be one of the most complex systems. Again, chaos, also called as “butterfly effect,” limits our ability to make weather predictable. So, it is not easy to predict rainfall by conventional machine learning approaches. However, several kinds of research have been proposed to predict rainfall by using different computational methods. To accomplish chaotic rainfall prediction system for Bangladesh, in this study, historical data set-driven long short term memory (LSTM) networks method has been used, which overcomes the complexities and chaos-related problems faced by other approaches. The proposed method has three principal phases: (i) The most useful 10 features are chosen from 20 data attributes. (ii) After that, a two-layer LSTM model is designed. (iii) Both conventional machine learning approaches and recent works are compared with the LSTM model. This approach has gained 97.14% accuracy in predicting rainfall (in millimeters), which outperforms the state-of-the-art solutions. Also, this work is a pioneer work to the rainfall prediction system for Bangladesh.
Dissertations / Theses on the topic "Accurate rainfall prediction"
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Mahmood, Arshad. "Rainfall prediction in Australia : Clusterwise linear regression approach." Thesis, Federation University Australia, 2017. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251.
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Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.Doctor of Philosophy
Books on the topic "Accurate rainfall prediction"
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Cook, Kerry H. Climate Change Scenarios and African Climate Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.545.
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Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in improving model projections is comparisons with observations. Exceeding uncertainties associated with climate model simulation are uncertainties due to projections of future emissions of CO2 and other greenhouse gases. Humanity’s choices in emissions pathways will have profound effects on climate, especially after the mid-century.The African Sahel is a transition zone characterized by strong meridional precipitation and temperature gradients. Over West Africa, the Sahel marks the northernmost extent of the West African monsoon system. The region’s climate is known to be sensitive to sea surface temperatures, both regional and global, as well as to land surface conditions. Increasing atmospheric greenhouse gases are already causing amplified warming over the Sahara Desert and, consequently, increased rainfall in parts of the Sahel. Climate model projections indicate that much of this increased rainfall will be delivered in the form of more intense storm systems.The complicated and highly regional precipitation regimes of East Africa present a challenge for climate modeling. Within roughly 5º of latitude of the equator, rainfall is delivered in two seasons—the long rains in the spring, and the short rains in the fall. Regional climate model projections suggest that the long rains will weaken under greenhouse gas forcing, and the short rains season will extend farther into the winter months. Observations indicate that the long rains are already weakening.Changes in seasonal rainfall over parts of subtropical southern Africa are observed, with repercussions and challenges for agriculture and water availability. Some elements of these observed changes are captured in model simulations of greenhouse gas-induced climate change, especially an early demise of the rainy season. The projected changes are quite regional, however, and more high-resolution study is needed. In addition, there has been very limited study of climate change in the Congo Basin and across northern Africa. Continued efforts to understand and predict climate using higher-resolution simulation must be sustained to better understand observed and projected changes in the physical processes that support African precipitation systems as well as the teleconnections that communicate remote forcings into the continent.
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Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190676889.013.16.
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As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.
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Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190699420.013.16.
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As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.
Book chapters on the topic "Accurate rainfall prediction"
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Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation, 1613–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.
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AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.
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Takasao, T., M. Shiiba, and E. Nakakita. "A Real-Time Estimation of the Accuracy of Short-Term Rainfall Prediction Using Radar." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 339–51. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1072-3_26.
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Hadid, Baya, and Eric Duviella. "Influence of the Predictive Rainfall/Runoff Model Accuracy on an Optimal Water Resource Management Strategy." In Informatics in Control, Automation and Robotics, 168–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31993-9_8.
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P., Umamaheswari. "Water-Level Prediction Utilizing Datamining Techniques in Watershed Management." In Advances in IT Standards and Standardization Research, 261–75. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9795-8.ch017.
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The massive wastage of water occurs due to irregular heavy rainfall and water released from dams. Many statistical methods are of the previous techniques used to predict water level, which give approximate results. To overcome this disadvantage, gradient descent algorithm has been used. This gives more accurate results and provides higher performance. K-means algorithm is used for clustering, which iteratively assigns each data point to one of the k groups according to the given attribute. The clustered output will be refined for further processing in such a way that the data will be extracted as ordered datasets of year-wise and month-wise data. Clustering accuracy has been improved to 90.22%. Gradient descent algorithm is applied for reducing the error. It also helps in predicting the amount of water to be stored in watershed for future usage. Watershed development appears to be helpful in terms of groundwater recharge, which benefits the farmers. It can also be used for domestic purposes.
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Maejima, Yasumitsu. "On the Use of the Ensemble Kalman Filter for Torrential Rainfall Forecasts." In Floods - Understanding Existing and Emerging Risk Drivers in a Climate Change Context [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.107916.
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Torrential rainfall is a threat to modern human society. To prevent severe disasters by the torrential rains, it is an essential to accurate the numerical weather prediction. This article reports an effort to improve torrential rainfall forecasts by the Ensemble Kalman Filter based on the recent studies. Two series of numerical experiments are reported in this chapter. One is a dense surface observation data assimilation for a disastrous rainfall event caused by active rainband maintained for a long time. Although an experiment with a conventional observation data set represents the rainband, the significant dislocation and the underestimated precipitation amount are found. By contrast, dense surface data assimilation contributes to improve both the location and surface precipitation amount of the rainband. The other is the rapid-update high-resolution experiment with every 30-second Phased Array Weather Radar (PAWR) data for an isolated convective system associated with a local torrential rain. The representation of this event is completely missed without the PAWR data, whereas the active convection is well represented including fine three-dimensional structure by PAWR data assimilation. Throughout these studies, the data assimilation by Ensemble Kalman Filter has a large positive impact on the forecasts for torrential rainfall events.
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Stipaničev, Darko, Marin Bugarić, Nera Bakšić, and Darko Bakšić. "Fuel Moisture Content in Croatian wildfire spread simulator AdriaFirePropagator." In Advances in Forest Fire Research 2022, 216–21. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_35.
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Fuel moisture content (FMC) is the mass of water contained within vegetation in relation to the dry mass. It is one of the most important variables in all wildfire prediction and spread simulation models. FMC has great influence on wildfire ignition and combustion. For accurate wildfire spread simulations and wildfire risk index estimations, fuel moisture is a very important input variable. Since 2016, Croatian firefighters in everyday practice use Web based System for wildfire behaviour modelling and wildfire spread simulation named AdriaFirePropagator. The simulator is based on semi-empirical Rothermel’s surface fire spread model for wildfire behaviour modelling and cellular automata for wildfire spread simulation. Fuel moisture, both live and dead is a very sensitive parameter in wildfire behaviour modelling. Live fuel moisture defines the moisture content of live fuels and dead fuel moisture is defined as moisture of dead fuels with time-lag of 1 hour, 10 hour and 100 hours. In Croatia there is no organised service for daily measurement of fuel moisture content, so values of these variables has to be estimated from meteorological parameters. This paper compares three approaches to fine dead fuel estimation, all implemented in AdriaFirePropagator. The first one, used in most wildfire simulation software, was based on standard mathematical models that relate air temperature, air humidity, 24-hours rainfall and wind speed with fine dead fuel moisture (FFMC). The second one was based on standard Fire Behavior Analysis Tables (FBA Tables) and the third one was based on intensive experimental research of dead fine fuel moisture content of Aleppo pine (Pinus halepenses Mill.). After intensive experimental research new Croatian fine dead fuel models PhFFMC was developed, tested in selected pine species stand and applied in AdriaFirePropagator for fuel regions where this pine species dominate. Croatian model is much better correlated with experimental data, therefore for more accurate wildfire simulations, similar models have to be developed also for other typical vegetation fuel types.
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Hartomo, Kristoko Dwi, Sri Yulianto Joko Prasetyo, Muchamad Taufiq Anwar, and Hindriyanto Dwi Purnomo. "Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern." In Computational Intelligence in the Internet of Things, 234–55. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7955-7.ch010.
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The traditional crop farmers rely heavily on rain pattern to decide the time for planting crops. The emerging climate change has caused a shift in the rain pattern and consequently affected the crop yield. Therefore, providing a good rainfall prediction models would enable us to recommend best planting pattern (when to plant) in order to give maximum yield. The recent and widely used rainfall prediction model for determining the cropping patterns using exponential smoothing method recommended by the Food and Agriculture Organization (FAO) suffered from short-term forecasting inconsistencies and inaccuracies for long-term forecasting. In this study, the authors developed a new rainfall prediction model which applied exponential smoothing onto seasonal planting index as the basis for determining planting pattern. The results show that the model gives better accuracy than the original exponential smoothing model.
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Hudnurkar, Shilpa, Vidur Sood, Vedansh Mishra, Manobhav Mehta, Akash Upadhyay, Shilpa Gite, and Neela Rayavarapu. "Multivariate Time Series Forecasting of Rainfall Using Machine Learning." In Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis, 87–106. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3981-4.ch007.
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Predicting rainfall is essential for assessing the impact of climatic and hydrological changes over a specific region, predicting natural disasters or day-to-day life. It is one of the most prominent, complex, and essential weather forecasting and meteorology tasks. In this chapter, long short-term memory network (LSTM), artificial neural network (ANN), and 1-dimensional convolutional neural network LSTM (1D CNN-LSTM) models are explored for predicting rainfall at multiple lead times. The daily weather parameter data of over 15 years is collected for a station in Maharashtra. Rainfall data is classified into three classes: no-rain, light rain, and moderate-to-heavy rain. The principal component analysis (PCA) helped to reduce the input feature dimension. The performance of all the networks are compared in terms of accuracy and F1 score. It is observed that LSTM predicts rainfall with consistent accuracy of 82% for 1 to 6 days lead time while the performance of 1D CNN-LSTM and ANN are comparable to LSTM.
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Iliger, Sushma, and Mario Pinto. "Comparative Study of Rainfall Prediction Modeling Techniques : Case Study on Karapur, India." In Artificial Intelligence and Communication Technologies, 159–66. Soft Computing Research Society, 2022. http://dx.doi.org/10.52458/978-81-955020-5-9-16.
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India's economy is highly dependent on agriculture which is in turn dependent on rainfall. Every region receives varying amounts of rainfall and based on that the crops are grown to suit the geographical conditions. Due to the lack of proper predictive technology in making use of rainfall, the agricultural practices have put high pressure on the underground water tables leading to depletion of water resources in local areas. Considering these factors, we have tested the suitability of the different existing machine learning models for the data collected from the Karapur weather station of Goa state, India to predict rainfall locally. The different models being used are Multiple Linear regression(MLR), Decision tree Regressor, Random forest Regressor, XGBoost, and Artificial Neural Network algorithm(ANN). After analysis we obtained 85% accuracy and the algorithm performance compared in terms of mean absolute error and R2 Score. . The result of the study revealed that ANN outperformed the others by delivering an average R2 score close to 1.
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Bandason, Tsitsi. "Harnessing Radio and Internet Systems to Monitor and Mitigate Agricultural Droughts in Rural African Communities." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0031.
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Humankind has not yet discovered a way to prevent drought entirely. Hence, the provision of timely and accurate climate and weather information can help rural and semiurban producers to better prepare for and mitigate the effects of insufficient precipitation (IRI, 2001). Communicating drought information to remote rural populations, however, has been a major challenge in Africa (Stern and Easterling, 1999). Seasonal rainfall forecasts, precipitation, and stream flow monitoring products, key environmental information, and even lifesaving early warnings are commonly trapped in the information bottleneck of Africa’s capital cities, due to the relative lack of infrastructure in rural areas (Glantz, 2001). Without access to reliable communication networks, the majority of Africa’s farmers and herders are cut off from the scientific and technological advances that support agricultural decision-making in other parts of the world. Before the proliferation of radios, cell phones, and televisions, Africans used local methods—interpreting wind speed and direction, cloud formations, vegetation, and insect and bird migrations, for example—to predict weather patterns and the advent or cessation of precipitation. This chapter describes a Radio and Internet (RANET; http://www.ranetproject.net) system for communicating drought information to the rural communities in Niger and Uganda. This system was developed under a disaster mitigation program funded by the U.S. Agency for International Development (USAID). The need for a drought communications system tailored to the realities of rural Africa was initially communicated to the director of the African Centre of Meteorological Applications for Development (ACMAD; http:// www.acmad.ne) by a nomad in the desert of southeastern Algeria when he declined the gift of a radio offered by the young meteorologist researching desert locusts near Djanet. The nomad did agree that information was vital to his survival. “Just tell me where it has rained. I will know where to take my flocks” (personal communication with Boulahya, Hirir, Algeria, February 1988). He explained that he was familiar with every rise and fall of the terrain and would lead his animals every rainy season to meet the water as it flowed in streams to form pools at low spots in the landscape.
Conference papers on the topic "Accurate rainfall prediction"
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Üneş, Fatih, Mustafa Demirci, Yunus Ziya Kaya, Eyup Ispir, and Mustafa Mamak. "Groundwater Level Prediction Using Support Vektor Machines and Autoregressive (AR) Modelss." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.093.
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Water resources managers can benefit from accurate prediction of the availability of groundwater. Ground water is a major source of water in Turkey for irrigation, water supply and industrial uses. The ground water level fluctuations depend on several factors such as rainfall, temperature, pumping etc. In this study, Hatay Amik Plain, Kumlu region was evaluated using Autoregressive (AR) and Support Vektor Machines (SVMs) methods. The monthly groundwater level was used the previous years data belonging to the Kumlu region.
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Vieux, B. E., and P. B. Bedient. "Hydrologic Prediction Accuracy Assessment Using Radar Rainfall." In World Water and Environmental Resources Congress 2004. Reston, VA: American Society of Civil Engineers, 2004. http://dx.doi.org/10.1061/40737(2004)273.
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Shah, Urmay, Sanjay Garg, Neha Sisodiya, Nitant Dube, and Shashikant Sharma. "Rainfall Prediction: Accuracy Enhancement Using Machine Learning and Forecasting Techniques." In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2018. http://dx.doi.org/10.1109/pdgc.2018.8745763.
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KIM, Jaekwang. "Seasonal Heavy Rain Forecasting Method." In 2nd International Conference on Soft Computing, Artificial Intelligence and Machine Learning (SAIM 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111002.
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In this study, we study the technique for predicting heavy / non-rain rainfall after 6 hours from the present using the values of the weather attributes. Through this study, we investigated whether each attribute value is influenced by a specific pattern of weather maps representing heavy and non-heavy rains or seasonally when making heavy / non-heavy forecasts. For the experiment, a 20-year cumulative weather map was learned with Support Vector Machine (SVM) and tested using a set of correct answers for heavy rain and heavy rain. As a result of the experiment, it was found that the heavy rain prediction of SVM showed an accuracy rate of up to 70%, and that it was seasonal variation rather than a specific pattern that influenced the prediction.
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Fadhli, Nanda, Aji Hamim Wigena, and Anik Djuraidah. "Determination of General Circulation Model Domain Using LASSO to Improve Rainfall Prediction Accuracy in West Java." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290466.
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Srinivasa Ramanujam, K., and C. Balaji. "A Fast Polarized Microwave Radiative Transfer Model for a Raining Atmosphere." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22228.
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Retrieval of vertical rain structure and hence the estimation of surface rain rate is of central importance to various missions involving remote sensing of the earth’s atmosphere. Typically, remote sensing involves scanning the earth’s atmosphere at visible, infra red and microwave frequencies. While the visible and infra red frequencies can scan the atmosphere with higher spatial resolution, they are not suited for scanning under cloudy conditions as clouds are opaque under these frequencies. However, the longer wavelength microwave radiation can partially penetrate through the clouds without much attenuation thereby making it more suitable for meteorological purposes. The retrieval algorithms used for passive microwave remote sensing involve modeling of the radiation in the earth’s atmosphere where in the clouds and precipitating rain (also known as hydrometeors) emit / absorb / scatter. Additionally, it has been observed that the rain droplets tend to polarize the microwave signal emitted by the earth’s surface. In view of this, the first step in the development of a rainfall retrieval algorithm for any satellite mission is to simulate the radiances (also known as brightness temperatures) that would have been measured by a typical radiometer for different sensor frequencies and resolutions. Towards this, a polarized microwave radiation transfer code has been developed in house for a plane parallel raining atmosphere (henceforth called as forward model) that depicts the physics as seen by a satellite. Physics based retrieval algorithm often involves repeated execution of the forward model for various raining scenario. However, due to the complexity involved in the radiation modeling of the raining atmosphere which is participating in nature, the forward model suffers from the drawback that it requires enormous computational effort. In the present work, a much quicker alternative is proposed wherein the forward model can be replaced with an Artificial Neural Network (ANN) based Fast Forward Model (AFFM). This AFFM can be used in conjunction with an appropriate inverse technique to retrieve the rain structure. Spectral microwave brightness temperatures at frequencies corresponding to the Tropical Rainfall Measuring Mission (TRMM) of National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) are first simulated using an in-house polarized radiate on transfer code for sixteen past cyclones in the North Indian Ocean region in the period (2000–2005), using the hydrometeor profiles retrieved from the Goddard Profiling Algorithm (GPROF) of the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI). This data is split into two sets: while the first set of data is used for training the network, the remainder of the data is used for testing the ANN. The results obtained are very encouraging and shows that neural network is capable of predicting the brightness temperature accurately with the correlation coefficient of over 99%. Furthermore, the execution of the forward model on an Intel Core 2 Quad 3.0 GHz processor based, 8 GB DDR3 RAM workstation took 3 days, while the AFFM delivers the results in 10 seconds.