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SEVEN MOST
TENABLE
APPLICATIONS OF AI
ON WATER RESOURCE
MANAGEMENT
Dr.Mrinmoy Majumder
Amazon Author Page
http://www.mrinm...
WHAT IS AI ?
• Artificial intelligence (AI) is a wide-ranging branch of
computer science concerned with building smart
mac...
WHAT IS AI ?
CONTD.
• The term is frequently applied to the project of
developing systems endowed with the intellectual
pr...
AI ON WATER QUALITY
• Artificial intelligence is recently used to enhance remote
monitoring of water bodies by University ...
FLOOD
SUSCEPTIBILITY
• Flood susceptibility maps are crucial steps
for decision-makers to prevent and
manage disasters whi...
ESTIMATION OF EVAPOTRANSPIRATION
AND ITS COMPONENTS
• Evapotranspiration (ET) and its components of soil
evaporation (E) a...
DETERMINATION OF GROUND
WATER LEVEL
• Precise estimation of physical hydrology components including groundwater levels
(GW...
SOIL MOISTURE
• A new multi-model ensemble committee framework called
ANN-CoM was developed.
• ANN-CoM was evaluated in fo...
MONITORING
SUSPENDED
SEDIMENT
CONCENTRATIONS
• Purpose : The high costs of monitoring suspended
sediment concentration (SS...
SNOW DEPTH
• Purpose : “Accurate high spatial resolution snow depth mapping in arid and
semi-arid regions is of great impo...
CONCLUSION
• Six tenable examples of the application of Artificial Intelligence on
water resource problems were discussed....
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Seven Most tenable applications of AI o Water Resources Management

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AI or Artificial Intelligence is a pioneering technique that has enabled the creation of intelligent machines.or smart machines which has the power to self adapt based on the situation presented to it. It requires situations whose response is known and based on this training data set it learns the problems which it has to solve when it is ready. Due to the alarming success with AI in robotics, electronics etc fields the same technique is now used to solve the problems of water resource management.. This ppt shows seven most notable use of AI in water resources-based problems where satisfactory improvement has encouraged further application of the technique.

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Seven Most tenable applications of AI o Water Resources Management

  1. 1. SEVEN MOST TENABLE APPLICATIONS OF AI ON WATER RESOURCE MANAGEMENT Dr.Mrinmoy Majumder Amazon Author Page http://www.mrinmoymajumder.com
  2. 2. WHAT IS AI ? • Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. • Artificial intelligence is widely used to provide personalised recommendations to people, based for example on their previous searches and purchases or other online behaviour. • AI is hugely important in commerce: optimising products, planning inventory, logistics etc.
  3. 3. WHAT IS AI ? CONTD. • The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. • Basic logic behind AI is the information processing by a neural network which can adapt itself based on responses, just like human brain. • AI can also be used for solving water resource related problems as water resource problems are highly adaptive and changes with both time and space. • Some of the example application of AI is delineated in this presentation
  4. 4. AI ON WATER QUALITY • Artificial intelligence is recently used to enhance remote monitoring of water bodies by University of Stirling :– • A new algorithm was developed • Known as the 'meta-learning' method was developed to analyse the data directly from satellite sensors thus making it easier for coastal zone, environmental and industry managers to monitor issues such as harmful algal blooms (HABs) and possible toxicity in shellfish and finfish. Source : University of Stirling. "Artificial intelligence to monitor water quality more effectively." ScienceDaily. www.sciencedaily.com/releases/2021/05/210504112514.htm (accessed May 8, 2021).
  5. 5. FLOOD SUSCEPTIBILITY • Flood susceptibility maps are crucial steps for decision-makers to prevent and manage disasters which helps to identify the vulnerable regions. • Plenty of studies have used machine learning models to produce reliable susceptibility maps. • A novel method was developed to use the deep learning technique of LSTM to predict flood susceptibility • Appropriate feature engineering method with LSTM was integrated to predict flood susceptibility. • As a result not only temporal but also spatial accuracy of prediction get improved. Source : Fang, Zhice, Yi Wang, Ling Peng, and Haoyuan Hong. "Predicting flood susceptibility using LSTM neural networks." Journal of Hydrology 594 (2021): 125734.
  6. 6. ESTIMATION OF EVAPOTRANSPIRATION AND ITS COMPONENTS • Evapotranspiration (ET) and its components of soil evaporation (E) and vegetation transpiration (T), as key variables for the water-energy exchange between the land surface and the atmosphere, are widely used in hydrological and agricultural applications. • The land surface temperature based two-source energy balance (TSEB) model can provide high accuracy E, T and ET, which are spatio-temporally discontinuous, but spatio-temporally continuous daily ET is more helpful in water resources management. • This study attempted to develop a new combined model coupling the TSEB model and deep neural network (DNN) (TSEB_DNN) to improve the continuity of the prediction. • The TSEB_DNN model was found to be well consistent with the in situ measurements and had the overall correlation coefficient (R), root-mean-square-error (RMSE), and bias values of 0.88, 0.88 mm d-1, and 0.37 mm d-1, respectively. • The introduction of DNN improved the continuity of the prediction both in spatial and temporal scale. Source : Cui, Yaokui, Lisheng Song, and Wenjie Fan. "Generation of spatio-temporally continuous evapotranspiration and its components by coupling a two-source energy balance model and a deep neural network over the Heihe River Basin." Journal of Hydrology 597 (2021): 126176.
  7. 7. DETERMINATION OF GROUND WATER LEVEL • Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. • This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with the help of the variables :stream level, stream flow, precipitation, relative humidity and mean temperature. • The stream level was found to be the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds • “The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.”…Afzaal et.al.(2020). Source : Afzaal, Hassan, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, and Travis Esau. "Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning." Water 12, no. 1 (2020): 5.
  8. 8. SOIL MOISTURE • A new multi-model ensemble committee framework called ANN-CoM was developed. • ANN-CoM was evaluated in forecasting upper- and lower- layer soil moistures. • Volterra, M5 tree, random forest, and ELM served as comparison models. • Superior performance of ANN-CoM was recorded at all four study sites. • Seasonally, ANN-CoM generated better forecasts of lower layer soil moisture. • Source : Prasad, Ramendra, Ravinesh C. Deo, Yan Li, and Tek Maraseni. "Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors." Soil and Tillage Research 181 (2018): 63-81.
  9. 9. MONITORING SUSPENDED SEDIMENT CONCENTRATIONS • Purpose : The high costs of monitoring suspended sediment concentration (SSC) in rivers calls for development of indirect estimation methods, based on relationships with other variables which are easier and cheaper to obtain. • Input : drainage area, soil type, land use and cover and mean catchment slope turbidity, flow, precipitation and exponentially weighted moving average of past rainfall • Model used : Artificial Neural network (ANN) based models • Location : Brazilian part of the Upper Paraguay River Basin, • Conclusion : The proposed methodology allows the regional extrapolation of SSC to ungauged basins with very good performance, even in heterogeneous regions. • Source : Campos, Juliana Andrade, and Olavo Correa Pedrollo. "A regional ANN-based model to estimate suspended sediment concentrations in ungauged heterogeneous basins." Hydrological Sciences Journal just-accepted (2021).
  10. 10. SNOW DEPTH • Purpose : “Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modelling. “. But due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. • Inputs : The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. • Output : Downscaled snow depth data • Model Used : Deep Learning • Conclusion : “Downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.”… Zhu et.al.(2021). • Source : Zhu, Linglong, Yonghong Zhang, Jiangeng Wang, Wei Tian, Qi Liu, Guangyi Ma, Xi Kan, and Ya Chu. "Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning." Remote Sensing 13, no. 4 (2021): 584.
  11. 11. CONCLUSION • Six tenable examples of the application of Artificial Intelligence on water resource problems were discussed. • In all the studies, the most common observation was the noticeable improvement of the accuracy in the estimations from the deep learning model compared to the conventional numerical frameworks. • The capacity to adapt from the input data is the main advantage of using AI • But data requirement is also high. • Model developed with fewer data is as accurate as linear models • If very few data or examples are provided to the model for learning the inherent relationships that exist in between the input and output variables of the model AI based models will fail miserably. • In case of water resource problems data availability is the main constraint. • So intelligent application of AI based models hybridizing with another model may solve the problems, but it may also compromise the adaptable capacity of AI models.

AI or Artificial Intelligence is a pioneering technique that has enabled the creation of intelligent machines.or smart machines which has the power to self adapt based on the situation presented to it. It requires situations whose response is known and based on this training data set it learns the problems which it has to solve when it is ready. Due to the alarming success with AI in robotics, electronics etc fields the same technique is now used to solve the problems of water resource management.. This ppt shows seven most notable use of AI in water resources-based problems where satisfactory improvement has encouraged further application of the technique.

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