Water Preservation in Soan River Basin using Deep Learning Techniques

June 26, 2019 · Declared Dead · 🏛 arXiv.org

Repo contents: 1980-2000.xlsx, 2001-2011.xlsx, CKLRRR08.TXT, CKLRRR09.TXT, CKLRRR10.TXT, Flow data of Soan Basin.xlsx, GDPRRR11.TXT, ISLRRR01.TXT, ISLRRR02.TXT, ISLRRR03.TXT, ISLRRR04.TXT, ISLRRR05.TXT, ISLRRR06.TXT, ISLRRR07.TXT, ISLRRR2K.TXT, ISLRRR80.TXT, ISLRRR81.TXT, ISLRRR82.TXT, ISLRRR83.TXT, ISLRRR84.TXT, ISLRRR85.TXT, ISLRRR86.TXT, ISLRRR87.TXT, ISLRRR88.TXT, ISLRRR89.TXT, ISLRRR90.TXT, ISLRRR91.TXT, ISLRRR92.TXT, ISLRRR93.TXT, ISLRRR94.TXT, ISLRRR95.TXT, ISLRRR96.TXT, ISLRRR97.TXT, ISLRRR98.TXT, ISLRRR99.TXT, Islamabad Aairport Daily Temp.xlsx, MURRRR01.TXT, MURRRR02.TXT, MURRRR03.TXT, MURRRR04.TXT, MURRRR05.TXT, MURRRR06.TXT, MURRRR07.TXT, MURRRR08.TXT, MURRRR09.TXT, MURRRR10.TXT, MURRRR11.TXT, MURRRR2K.TXT, MURRRR80.TXT, MURRRR81.TXT, MURRRR82.TXT, MURRRR83.TXT, MURRRR84.TXT, MURRRR85.TXT, MURRRR86.TXT, MURRRR87.TXT, MURRRR88.TXT, MURRRR89.TXT, MURRRR90.TXT, MURRRR91.TXT, MURRRR92.TXT, MURRRR93.TXT, MURRRR94.TXT, MURRRR95.TXT, MURRRR96.TXT, MURRRR97.TXT, MURRRR98.TXT, MURRRR99.TXT, Murree daily Temp.xlsx, Murree daily ppt.xlsx, README.md

Authors Sadaqat ur Rehman, Zhongliang Yang, Muhammad Shahid, Nan Wei, Yongfeng Huang, Muhammad Waqas, Shanshan Tu, Obaid ur Rehman arXiv ID 1906.10852 Category cs.NE: Neural & Evolutionary Citations 5 Venue arXiv.org Repository https://github.com/sadaqat007/Dataset Last Checked 1 month ago

Abstract

Water supplies are crucial for the development of living beings. However, change in the hydrological process i.e. climate and land usage are the key issues. Sustaining water level and accurate estimating for dynamic conditions is a critical job for hydrologists, but predicting hydrological extremes is an open issue. In this paper, we proposed two deep learning techniques and three machine learning algorithms to predict stream flow, given the present climate conditions. The results showed that the Recurrent Neural Network (RNN) or Long Short-term Memory (LSTM), an artificial neural network based method, outperform other conventional and machine-learning algorithms for predicting stream flow. Furthermore, we analyzed that stream flow is directly affected by precipitation, land usage, and temperature. These indexes are critical, which can be used by hydrologists to identify the potential for stream flow. We make the dataset publicly available (https://github.com/sadaqat007/Dataset) so that others should be able to replicate and build upon the results published.