MACHINE LEARNING APPROACH TO FORECASTING


Machine learning approach for forecasting crop yield based on climatic parameters
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With the impact of climate change in India, majority of the agricultural crops are being badly affected interms of their performance over a period of last two decades. Predicting the crop yield well ahead of its harvest would help the policy makers and farmers for taking

Forecasting domestic violence: A machine learning approach to help inform arraignment decisions
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Arguably the most important decision at an arraignment is whether to release an offender until the date of his or her next scheduled court appearance. Under the Bail Reform Act of 1984, threats to public safety can be a key factor in that decision. Implicitly, a forecast of

Convolutional LSTM network: A machine learning approach for precipitation nowcasting
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learning approach , especially deep learning , to the challenging precipitation nowcasting problem which so far has not benefited from sophisti- cated machine learning techniques. We formulate precipitation nowcasting as a spatiotemporal se- quence forecasting problem and

Machine learning methods for solar radiation forecasting : A review
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paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches is complicated due to the diversity of the data set, time step, forecasting horizon, set authors proposed the use of hybrid models or to use an ensemble forecast approach

Forecasting heat load for smart district heating systems: A machine learning approach
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The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi-family apartment buildings

Medium-term urban water demand forecasting with limited data using an ensemble wavelet bootstrap machine – learning approach
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Accurate and reliable weekly and monthly water demand forecasting is important for effective and sustainable planning and use of urban water supply infrastructure. This study explored a hybrid wavelet bootstrap artificial neural network (WBANN) modeling approach This work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach . The

Machine learning strategies for time series forecasting
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to the formulation (5) as a general representation of the time series which includes as particular instance also the case (4). The success of a reconstruction approach starting from a 3 Machine Learning Approaches to Model Time Dependencies 3.1 Supervised Learning Setting

Quantitative forecasting of PTSD from early trauma responses: A machine learning application
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Fig. 2. Machine Learning approach for feature selection and classification accuracy in this study is consistent with AUCs obtained in other psychiatric forecasting and classification value, their limited consistency may be due to inherent limitations of their modeling approaches Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature, traditional and artificial intelligence models have been applied to this task. An attempt to explore and develop better expert models is an ongoing endeavor for this hydrological We investigate the use of modern machine – learning techniques for weather prediction. The AIM(Abductory Induction Mechanism) tool for the Macintosh computer has been used for modelling and 3-day forecasting of the minimum temperature in the Dhahran region

Application of machine learning techniques for supply chain demand forecasting
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In our investigation of the feasibility and comparative analysis of machine learning approaches to forecasting manufacturers distorted demand, we will use concrete tools, including Neural Networks (NN), Recurrent Neural Networks (RNN), and Support Vector Machines (SVM)

An empirical comparison of machine learning models for time series forecasting
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complexity. For machine learning approaches such criteria are not well-developed yet applications. The dominant approach in the machine learning literature has been to use the K-fold validation approach for model selection. Empirical [HTML]

Machine learning techniques in disease forecasting : a case study on rice blast prediction
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based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion. Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases

A machine learning approach to finding weather regimes and skillful predictor combinations for short-term storm forecasting
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A major challenge for efficient flight planning and air traffic management is the accurate forecasting of weather that poses a danger to aviation. In support of the Joint Planning and Development Office (JPDO) vision of a single, authoritative source of weather information for

A machine learning approach to finding weather regimes and skillful predictor combinations for short-term storm forecasting
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A major challenge for efficient flight planning and air traffic management is the accurate forecasting of weather that poses a danger to aviation. In support of the Joint Planning and Development Office (JPDO) vision of a single, authoritative source of weather information for

Non-Linear Machine Learning Approach to Short-Term Precipitation Forecasting
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Using machine learning based model is a promising way to solve the challenging precipitation forecasting problem. The powerful computational capabilities of cloud computing enables us to investigate increasingly complex phenomena from multiple data

A machine learning -based approach to forecasting alcoholic relapses
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This research aims to explore alcoholic relapses by modelling four types of machine learning algorithms on clinical trial data of patients in an alcohol addiction treatment plan provided by an Uppsala-based company called Kontigo Care, with the goal of predicting



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