Water Preservation in Soan River Basin using Deep Learning Techniques

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 (this https URL) so that others should be able to replicate and build upon the results published.

[1]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[2]  Huilin Gao,et al.  Effects of Urbanization and Climate Change on Peak Flows over the San Antonio River Basin, Texas , 2016 .

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Z. Cong,et al.  Understanding the impacts of climate change and human activities on streamflow: a case study of the Soan River basin, Pakistan , 2018, Theoretical and Applied Climatology.

[5]  J. Friedman.  Stochastic gradient boosting , 2002 .

[6]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Hui Li,et al.  Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation , 2018, Water.

[8]  Casey Brown,et al.  The Indus Basin of Pakistan: The Impacts of Climate Risks on Water and Agriculture , 2013 .

[9]  Paolo Salvaneschi,et al.  Diagnosing Ancient Monuments with Expert Software , 1997 .

[10]  C S P Ojha,et al.  Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[11]  Jennifer Sills.  NextGen VOICES: Unique identities. , 2019, Science.

[12]  S. Guikema,et al.  Statistical Analysis of the Effectiveness of Seawalls and Coastal Forests in Mitigating Tsunami Impacts in Iwate and Miyagi Prefectures , 2016, PloS one.

[13]  Stefan Julich,et al.  Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya , 2016 .

[14]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[15]  Hongqing Li,et al.  Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network , 2018, Water.

[16]  Dongwoo Jang,et al.  Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems , 2018 .

[17]  Francesca Pianosi,et al.  Short Term Reservoirs Operation On The Seine River: Performance Analysis Of Tree-Based Model Predictive Control , 2014 .

[18]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[19]  Yongfeng Huang,et al.  Optimization of CNN through Novel Training Strategy for Visual Classification Problems , 2018, Entropy.

[20]  Yongfeng Huang,et al.  CSFL: A novel unsupervised convolution neural network approach for visual pattern classification , 2017, AI Communications.

[21]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[22]  Seth D Guikema,et al.  Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes , 2011, Risk analysis : an official publication of the Society for Risk Analysis.

[23]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[24]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[25]  A. Pistocchi,et al.  Human pressures and ecological status of European rivers , 2017, Scientific Reports.

[26]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[27]  Jean-Luc Probst,et al.  Evidence for global runoff increase related to climate warming , 2004 .

[28]  Mirai Watanabe,et al.  Varying sensitivity of mountainous streamwater base-flow \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\bf{N}}{{\bf , 2017, Scientific Reports.

[29]  R. Betts,et al.  Detection of a direct carbon dioxide effect in continental river runoff records , 2006, Nature.

[30]  T. R. Petty,et al.  Streamflow Hydrology Estimate Using Machine Learning (SHEM) , 2018 .

[31]  Jiming Jin,et al.  Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China , 2017 .

[32]  Shih-Chieh Kao,et al.  Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States , 2018 .

[33]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[34]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[35]  Amir Mosavi,et al.  Flood Prediction Using Machine Learning, Literature Review , 2018 .