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Background In later 2019 and early 2020, Australia faced devastating bushfires started in late 2019, which swiftly got worse before rains helped contain many of the worst fires in February 2020. Bush fires are major environmental issues, creating economic and ecological damages. It is reported that Australia's catastrophic bushfire crisis has destroyed thousands of homes, burned millions hectares of forest, and taken an enormous toll on wildlife. Therefore, fast and automatic detection of bushfires at an early stage is crucial for a successful firefighting.Traditional human surveillance is expensive and inefficient, which can also be affected by subject factors. With the advances in information technologies, a variety of data about the forest can be collected, such as remote images collected by satellites and meteorological data collected by local sensors. The collected data contains rich information about the status of the forest, the analysis of which can help us detect potential bushfires so as to make effective and efficient firefighting plan and then minimize the damage caused by the bushfires.In this project you are given a bushfire dataset and an article that uses this dataset. The authors have developed several ML models for predicting the burned area of a bushfire and compared their performance. You must read the article to understand the problem, the dataset, and the methodology to complete the following tasks.Dataset The dataset contains 517 fire instances, each of which have 13 columns: the first 12 columns corresponding to the attributes (e.g., spatial coordinates, month, day, four fire indices, and other meteorological data) and the last column containing the burned area, i.e., the variable that we will predict. The details of the dataset can be found in the original research paper. The dataset files are stored in UCI's website below (click the hyper-link to download the data) Forest fires data : There are two files on the website. One called “forest-fires.csv” contains the data needed for the analysis, and another called “forestfires.names" contains the information about the dataset. Tasks: 1. Read the article and reproduce the RMSE results presented in Table 3 using Python modules and packages (including your own script or customised codes). Write a report summarising the dataset, used ML methods, experiment protocol and results including variations, if any. During reproducing the results: (15 Marks) i) you should use the same set of features used by the authors. ii) you should use the same classifier with exact parameter values. iii) you should use the same training/test splitting approach as used by the authors. iv) you should use the same pre/post processing, if any, used by the authors. v) you should report the same performance metric (RMSE) as shown in Table 3.N.B. (i) If some of the ML methods are not covered in the current unit. Consider them as HD tasks i.e., based on the knowledge gained in the unit you should be able to find necessary packages and modules to reproduce the results. (ii) If you find any issue in reproducing results or some subtle variations are found due to implementation differences of packages and modules in Python then appropriate explanation of them will be considered during evaluation of your submission. (iii) Similarly, variation in results due to randomness of data splitting will also be considered during evaluation based on your explanation. (iii) Obtained marks will be proportional to the number of ML methods that you will report in your submission with correctly reproduced results. http://www3.dsi.uminho.pt/pcortez/fires.pdfhttps://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/(iv) Make sure your Python/R code segment generates the reported results, otherwise you will receive zero marks for this task.Marking criteria: i) Unsatisfactory (x<6): tried to implement the methods but unable to follow the approach presented in the article. Variation of marks in this group will depend on the quality of report. ii) Fair (6<=x<9): appropriately implemented 50% of the methods presented in the article. Variation of marks in this group will depend on the quality of report. iii) Good (9<=x<12): appropriately implemented 70% of the methods presented in the article. Variation of marks in this group will depend on the quality of report. iv) Excellent(x>=12): appropriately implemented >=90% of the methods presented in the article. Variation of marks in this group will depend on the quality of report.2. Design and develop your own ML solution for this problem. The proposed solution should be different from all approaches mentioned in the provided article. This does not mean that you must have to choose a new ML algorithm. You can develop a novel solution by changing the feature selection approach or parameter optimisations process of used ML methods or using different ML methods or adding regularization or different combinations of them. This means, the proposed system should be substantially different from the methods presented in the article but not limited to only change of ML methods. Compare the RMSE result with reported methods in the article. Write in your report summarising your solution design and outcomes. The report should include: (20 Marks) i) Motivation behind the proposed solution. ii) How the proposed solution is different from existing ones. iii) Detail description of the model including all parameters so that any reader can implement your model. iv) Description of experimental protocol. v) Evaluation metrics. vi) Present results using tables and graphs. vii) Compare and discuss results with respect to existing literatures. viii) Appropriate references (IEEE numbered).N.B. This is a HD (High Distinction) level question. Those students who target HD grade should answer this question (including answering all the above questions). For others, this question is an option. This question aims to demonstrate your expertise in the subject area and the ability to do your own research in the related area.Marking criteria: (i) Unsatisfactory (<10): an appropriate solution presented whose performance is lower than the reported performances in the article (Table 3). The variation in the marking in this group will depend on the quality of the report. (i) Fair (10 - <14): an appropriate solution presented whose performance is at least equal with the lowest performance reported in the article (Table 3). The variation in the marking in this group will depend on the quality of the report. (ii) Good (>=14): an appropriate solution presented whose performance is better than the best reported performances in the article (Table 3). The variation in the marking in this group will depend on the quality of the report. . https://www.bath.ac.uk/publications/library-guides-to-citing-referencing/attachments/ieee-style-guide.pdf
Answered Same DayJun 05, 2022

Answer To: no of words as brief as possible

Sathishkumar answered on Jun 06 2022
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