Rainfall Prediction a Novel Approach

The proposed framework is a prescient model for precipitation forecast. It is a framework which will foresee the future climate condition in light of the over a wide span of time climate conditions. The proposed framework makes utilization of more number of characteristics than the current framework which makes the forecast more precise. The framework utilizes the classifier calculation that is Naïve Bayes Algorithm for ordering the datasets. The system makes use of the huge dataset of weather condition of various location. The system uses the classifier algorithm called Decision tree classifier for classifying the dataset into a tree format which is used for the purpose of decision making

Rain Fall Prediction using Ada Boost Machine Learning Ensemble Algorithm

Every government takes initiative for the well-being of their citizens in terms of environment and climate in which they live. Global warming is one of the reason for climate change. With the help of machine learning algorithms in the flash light of Artificial Intelligence and Data Mining techniques, weather predictions not only rainfall, lightings, thunder outbreaks, etc. can be predicted. Management of water reservoirs, flooding, traffic - control in smart cities, sewer system functioning and agricultural production are the hydro-meteorological factors that affect human life very drastically. Due to dynamic nature of atmosphere, existing Statistical techniques (Support Vector Machine (SVM), Decision Tree (DT) and logistic regression (LR)) fail to provide good accuracy for rainfall forecasting. Different weather features (Temperature, Relative Humidity, Dew Point, Solar Radiation and Precipitable Water Vapour) are extracted for rainfall prediction. In this research work, data analysis using machine learning ensemble algorithm like Adaptive Boosting (Ada Boost) is proposed. Dataset used for this classification application is taken from hydrological department, India from 1901-2015. Overall, proposed algorithm is feasible to be used in order to qualitatively predict rainfall with the help of R tool and Ada Boost algorithm. Accuracy rate and error false rates are compared with the existing Support Vector Machine (SVM) algorithm and the proposed one gives the better result

Rainfall Forecasting Using Backpropagation Neural Network

Rainfall already became vital observation object because it affects society life both in rural areas or urban areas. Because parameters to predict rainfall rates is very complex, using physics based model that need many parameters is not a good choice. Using alternative approach like time-series based model is a good alternative. One of the algorithm that widely used to predict future events is Neural Network Backpropagation. On this research we will use Nguyen-Widrow method to initialize weight of Neural Network to reduce training time. The lowest MSE achieved is {0,02815;  0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer

Predicción temperatura Bogotá: enfoque estocástico vs aprendizaje profundo (Deep learning)

Mejorar la predicción de las variables climáticas en el corto, mediano y largo plazo es un objetivo que toma cada día mayor relevancia dentro de las agendas de trabajo de gobiernos alrededor del mundo, esto debido al impacto que tiene en actividades humanas, tales como la agricultura, la generación de energía, la construcción, entre otras más [19]. Adicionalmente el auge de los estudios sobre calentamiento global y cambio climático que muestran escenarios adversos para la biodiversidad del planeta, estos han incrementado aún más el interés por fortalecer la capacidad de pronosticar el estado del sistema climático en todas las escalas espaciales y temporales posibles. El interés se convierte en necesidad cuando los datos muestran además un incremento de los eventos extremos como sequías, inundaciones, deslizamientos, incendios forestales entre otros [20]. Para mejorar los pronósticos tanto climáticos como meteorológicos se han planteado diferentes tipos de modelos matemáticos que estimen las posibles condiciones futuras sin embargo los procesos de validación de los resultados obtenidos evidencian que aún existen diferencias significativas entre las simulaciones y las observaciones especialmente a bajas resoluciones espaciales y en el corto plazo. Lo anterior exige la continua exploración de nuevos modelos o metodologías que aporten información cada vez más confiable a la población que la requiere. Una de las variables climáticas que tienen mayor impacto en las actividades de la población es la precipitación, por tanto la mayoría de las investigaciones están dirigidas a predecir su comportamiento [21]. Otra de las variables que son relevantes en la dinámica atmosférica es la temperatura del aire, sin embargo, es menor el número de estudios sobre modelos para predecir su variabilidad. Por eso este proyecto se enfoca en el estudio de la temperatura diaria promedio por medio de modelos y metodologías que aún no han sido explorados con esta variable. Para hacer la implementación de los modelos se eligió la variable temperatura diaria promedio de la ciudad de Bogotá (Colombia) en particular del aeropuerto internacional El Dorado. Para predecir la variable mencionada se exploran modelos estocásticos tradicionales basados en regresión y modelos basados en el aprendizaje profundo y al final se comparan los resultados obtenidos por ambas opciones. En los últimos años se han visto avances importantes por parte del aprendizaje profundo (Deep Learning) modelando datos de series temporales [22], por tal motivo estos esquemas se incluyeron dentro de este caso de estudio [2].#PredicciónTemperaturaBogotáEnfoqueEstocásticoVsAprendizajeProfundo(DeepLearning)#PredicciónTemperaturaBogotáEnfoqueEstocástico#AprendizajeProfundo(DeepLearning)Improving the prediction of climatic variables in the short, medium and long term is an objective that is becoming increasingly important within the work agendas of governments around the world, due to the impact it has on human activities, such as agriculture. , power generation, construction, among others [19]. Additionally, the rise of studies on global warming and climate change that show adverse scenarios for the planet's biodiversity have further increased the interest in strengthening the ability to forecast the state of the climate system at all possible spatial and temporal scales. The interest becomes a necessity when the data also show an increase in extreme events such as droughts, floods, landslides, forest fires, among others [20]. To improve both climate and meteorological forecasts, different types of mathematical models have been proposed to estimate possible future conditions. However, the validation processes of the results obtained show that there are still significant differences between the simulations and the observations, especially at low spatial and spatial resolutions. in the short term. This requires the continuous exploration of new models or methodologies that provide increasingly reliable information to the population that requires it. One of the climatic variables that have the greatest impact on the activities of the population is precipitation, therefore most research is aimed at predicting its behavior [21]. Another of the variables that are relevant in atmospheric dynamics is air temperature, however, there are fewer studies on models to predict its variability. For this reason, this project focuses on the study of the average daily temperature through models and methodologies that have not yet been explored with this variable. To implement the models, the variable average daily temperature of the city of Bogotá (Colombia), in particular the El Dorado international airport, was chosen. To predict the mentioned variable, traditional stochastic models based on regression and models based on deep learning are explored and, in the end, the results obtained by both options are compared. In recent years, important advances have been seen in deep learning modeling time series data [22], for this reason these schemes were included in this case study [2]

A RAINFALL FORECASTING METHOD USING MACHINE LEARNING MODELS AND ITS APPLICATION TO THE FUKUOKA CITY CASE

In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models

Forecasting seasonal rainfall with copula modelling approach for agricultural stations in Papua New Guinea

Developing innovative forecasting tools is important to address issues related to climate change, agriculture, and economy of small Pacific Island nations. Papua New Guinea, PNG is a developing nation that is vulnerable to the imminent threats of climate change and influences agricultural sector that supports a majority of its citizens. Accurate modeling and forecasting methods for both monthly and seasonal rainfall (that influences agricultural and other human activities) by employing large-scale climate mode indices (linked to rainfall events) are significant predictive tools for developing climate resilience and productivity in agricultural activities. Copula statistical models, developed in this Master’s study, are considered as viable alternative tools to fulfill this objective. This Masters by Research Thesis utilizes the D-vine copula-based quantile regression methods that are developed to create a model between statistically significant lagged relationships and joint influences of large-scale climate mode indices such as the El-Niño Southern Oscillation (ENSO) and Indian Ocean Dipole- on seasonal rainfall data across four major agricultural-based weather stations. Copula techniques allow the respective model to fully capture the dependence structure between input(s) and the target variable regardless of the marginal distribution of each variable. The D-vine copula-based quantile approach, used in this study, through Akaike information criterion (AIC)-corrected conditional log-likelihood (cllAIC) can also enable researchers to identify the most influent predictor variables for seasonal rainfall forecasting. To forecast the monthly and the respective seasonal rainfall for PNG, an agricultural-reliant nation, the statistically significant lagged correlations between ENSO indicators (e.g., SOI, Nino3.0, etc.) and the IOD indicator (i.e., DMI) with a three-monthly total rainfall were established for up to 7 months ahead time. For example, in a 'lead-0' timescale case study for seasonal rainfall forecasting, this study has utilized the January to March average SOI (as a model input) relative to the April to June total rainfall (as the target variable) deduced by the Kendall rank correlation coefficients established between the input and the target variable. In terms of the results of this study, a correlation analysis performed between the most optimal lead times considering climate mode indices and the three-monthly total rainfall were found to be consistent with the most influent predictor variables identified from the D-vine copula-based quantile model (as a basis to generate bivariate models that captured ENSO impacts on rainfall). To further explore any improvements in rainfall forecast model accuracy, particularly, the extreme rainfall events, the study has also considered the impact of Indian Ocean Dipole (IOD) index by embedding the DMI into the bivariate model to finally construct a trivariate forecast models that accounts for compound effects of ENSO and IOD on extreme rainfall events. To ascertain the versatility of the proposed copula-based forecast models as a major contribution of this study, a number of statistical score metrics based on the Willmott's Index (d), Nash–Sutcliffe Efficiency (ENS), Legates-McCabe’s Index (L), root-mean-square-error (RMSE), and mean absolute error (MAE), including the Relative Root Mean Square Error (RRMSE) and Mean Absolute Percentage Error (MAPE) are computed from forecasted and observed rainfall data in the testing phase. It was evident that the station Aiyura attained the best result for both the bivariate and the trivariate model, exhibiting r = 0.63, RMSE = 105.99, MAE = 89.75, ENS = 0.63, d = 0.38, L=0.20 with, the RRMSE =15.39% for the bivariate study, whereas the trivariate model evaluations generated a score metric of 0.68, 0.42, 0.28 and 14.84%, respectively. In summary, the copula statistical modelling approaches contributed by this study, can be enabling mechanisms for climate change resilience, measuring and implementing risk management strategies. These predictive tools can have significant implications for applications in many socioeconomic sectors such as water resources management, better farming practices for crop health, and other agricultural management not only in the present study region but also in the other agricultural-reliant nations where rainfall prediction is often challenging task

Rainfall and runoff estimation using hydrological models and Ann techniques

Water is one of the most important natural resources and a key element in the socio-economic development of a State and Country. Water resources of the world in general and in India are under heavy stress due to increased demand and limitation of available quantity. Proper water management is the only option that ensures a squeezed gap between the demand and supply. Rainfall is the major component of the hydrologic cycle and this is the primary source of runoff. Worldwide many attempts have been made to model and predict rainfall behaviour using various empirical, statistical, numerical and deterministic techniques. They are still in research stage and needs more focussed empirical approaches to estimate and predict rainfall accurately. Various spatial interpolation techniques to obtain representative rainfall over the entire basin or sub-basins have also been used in the past. In the present work, estimation of mean rainfall over the Mahanadi basin lying in Odisha and its sub-basins has been done using different deterministic and geo-statistical methods including nearest neighbourhood, Spline, Inverse-distance weighting, and Kriging techniques. Different thematic maps for the study area have been developed for water resources assessment, planning and development analysis

Previsão de níveis de precipitação usando redes neurais artificiais

The planning of various human activities, such as agriculture, construction, transportation, tourism, leisure, among others, are delimited to a greater or lesser extent by the climatic conditions, especially rainfall amounts and temperature values. Climate forecast, i.e. forecast for months ahead, are negatively impacted by the dynamics of the atmosphere-earth-ocean system, causing various levels of uncertainties. Currently, many traditional and modern methods support the forecast of climate conditions; however, the necessary accuracy is not reached. Thus, in this thesis were analyzed how the artificial neural networks could contribute in the rainfall forecast. Artificial neural networks are a method of the artificial intelligence area that has had an accelerated development in recent decades, where a considerable number of applications with satisfactory results have positioned these networks as the state of the art in several areas of knowledge. Precipitation levels from three cities of Ecuador were forecasted using as predictors atmospheric and oceanic variables. The results obtained show that the artificial neural networks were able to predict the rain a month ahead with accuracy for Guayaquil of 89%, Portoviejo of 100% and Esmeraldas of 74%, results considered satisfactory and encouraging for the use of artificial intelligence techniques in the operational climatic forecast.O planejamento de diversas atividades humanas, tais como agricultura, construção, transporte, turismo, lazer, entre outras, são delimitadas em maior ou menor grau pelas condições climáticas, especialmente quantidades de chuvas e valores de temperaturas. As previsões climáticas, definidas como previsões de meses à frente, são impactadas negativamente pela dinâmica do sistema atmosfera-terra-oceano, causando diversos níveis de incertezas. Existem na atualidade vários métodos tradicionais e modernos que auxiliam na previsão das condições climáticas, mas que não a conseguem predizer com uma exatidão necessária. Assim, nesta tese foram analisadas como as redes neurais artificiais podem ajudar nas tarefas da previsão da chuva. As redes neurais artificiais são um método da área da inteligência artificial que tem tido um desenvolvimento acelerado nas décadas recentes, em que um número considerável de aplicações com resultados satisfatórios tem posicionado estas redes nas principais linhas de pesquisa em diversas áreas do conhecimento. Níveis de precipitação de três cidades do Equador foram prognosticados, utilizando como prognosticadores variáveis atmosféricas e oceânicas. Os resultados obtidos mostram que as redes neurais artificiais conseguiram predizer a chuva um mês à frente com exatidões para Guaiaquil de 89%, Portoviejo de 100% e Esmeraldas de 74%, resultados considerados satisfatórios e encorajadores para o uso de técnicas de inteligência artificial na previsão climática operacional

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced