Solving QLD's Droughts

Project Info

Team Name

Droughts 'R' Us

Team Members

4 members with unpublished profiles.

Project Description

Australia. In three words: beautiful, stunning, breathtaking. But, in a country so beautiful, it’s easy to forget. Forget that it isn’t so beautiful and easy everywhere in the nation. Forget those less fortunate. Less lucky.

60% of Queensland is in drought. Something as simple, and seemingly ‘plentiful’, as water has such a significant effect on a state. Farmers in particular are constrained and feel there is no way out. They feel there is no support for them and their situation.

Here at Droughts ‘R’ Us we want to help them. Together, we’ve been working to find a solution to this crisis.

Through the use of GovHack’s Dataset Open Portals, we’ve created a platform which is designed to pull and utilise the most current specific data points we want. The main data sets utilised came through the Queensland Government’s open source data platforms.

First used data set is from Queensland’s BOM open data source. The pulled information regards water storage in Queensland’s dams, from 2013 up until today. A created API was used extract all this information.

Second data set used is derived from the Queensland Spatial Catalogue. The geolocation of dam’s regarding their locality information was needed and was utilised by the use of GDB and shapefile. All this data also included map water storage information and all local council information that are currently in drought. Scripts were made to properly utilise this information.

The third used data set is from Queensland’s Long Paddock portal. This portal contained so much relevant data, mainly including drought information regarding soil moisture, precipitation, vapor pressures, temperatures, radiation levels and drought declaration areas.

All three data sets tying together to produce our informative platform to help out Queensland’s drought victims where we can. It is designed for both government bodies and farmers in Queensland, analysing numerous stats, but the main feature includes the study of past soil moisture trends, looking at the current levels, and predicting future drought levels for specific regions. This coming from Long Paddock’s historical data including temperature, precipitation, vapor pressure deficit to predict soil moisture.

Another feature includes the identification of the closest water deposits to the input’s location. Once you select your given council, and a certain range that you are after, it will show you all water deposits and dams within the range. Recommendations made, considering your location, water capacity and range. This will help drought victims find what they are after, looking at the best up to date stats.

Data Story

Not 100% sure what this is meant to be. Basically our data started on the websites and is now displayed in maps and lists through python and other functional usages.

Evidence of Work

Video

Team DataSets

Long Paddock Data

Description of Use 3.1 soil Moisture 3.2 precipitation 3.3 vapor pressure deceit 3.4 temperature 3.5 radiation 3.6 drought declaration area

Data Set

Seasonal Drought Prediction

Description of Use Use neural network with Historical data of soil, pr~ open datasources. To predict soil moisture to predict the drought in future. Agricualture drought which is closely related to soil moisture to assist farmer to make better decision on

Data Set

water storage information

Description of Use Create the python script the API to extract QLD dam storage information. for s in storageList: try: #s1 = "\'" + s + "\'" print(s1) params = { 'themeview': 'storages.summary', 'purpose': 'download', 'table': 'site_timeseries_table', 'filter_by': 'Queensland', 'table_filter': 'state', 'timeScale': 'latest', 'storageName': str(s), 'location': 'Queensland'} payload = { "Host": "www.bom.gov.au", "Connection": "keep-alive", "Content-Length": "169", "Origin": "http://www.bom.gov.au", "X-Requested-With": "XMLHttpRequest", "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.52 Safari/536.5", "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Accept": "application/json, text/javascript, */*; q=0.01", "Referer": "http://www.bom.gov.au/water/dashboards/", "Accept-Encoding": "gzip, deflate", "Accept-Language": "en-US,en;q=0.9", "Accept-Charset": "ISO-8859-1,utf-8;q=0.7,*;q=0.3", "Cookie": "hjid=1a4fc2a6-5910-4f26-bfc6-d83fb93ec10f; _ga=GA1.3.852307201.1567143122; _gid=GA1.3.1898364688.1567382730; ys-per-south-0=o%3Acollapsed%3Db%253A1; __utma=172860464.852307201.1567143122.1567482772.1567552351.10; __utmc=172860464; __utmz=172860464.1567552351.10.6.utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=(not%20provided); ak_bmsc=E4B7A6F38403AF395B1B9F92631537891720052B3B0E00005EF36E5DAFF43E6B~pl5kZWp+H3+EB/UCXhMg4bI/Vaq6WU0/EKB5irESsf+e5Z4tbhbRjxpZjJrM8dOepK5Oikbm7wNQsoh8OgOXWJIZkpHB8AjpxGSDmFii46UKO79cGTOTEZ84RmSb9DAP40Ibk4mElXFNvoqyW8swYZ60VzKxfn76Zh324HDzjPPTpoALYjHtx3QXPmsSMsE3Piq0FrsbHdWd9UGTjTLBGLIcg34lpyuPt2QC7J5R3e2pNbZOzEgj8sxMG4EeBd9gTl; _hjIncludedInSample=1; __utmb=172860464.22.9.1567552351; _gat_UA-96314402-1=1; bm_sv=EB626B58A54AD7B2FA9F83FB9676134E~eQ9NTTuoqMnzAvMriNuLrhFGl9piGq2jg595LrEP8iuWWOne0x9rVuLfgnlSZJXZNNPX+yHixiFz3zQ61hHVfbVjmx8VZ/ChaDf7d/HzD00dn2V/AudCKL48Za46V3ko3egLikH2+jTj1d3ZBcOmFVr24T5imzg5mOtCSX6Py9U=" } response = requests.post(url, data=params, headers=payload) print(response) response = response.json() print(response) header = response['header'] print(header) name = s+ '.csv' with open('./govhac/the location of water storage with capacity and availabe ml/' + name, 'w', newline='') as f: print(name) w = csv.writer(f) w.writerow(header) for data in response['body']: w.writerow(data) except Exception as e: print(str(e)) continue

Data Set