Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff  are essential in modeling t...

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2020.1715844

Predicting Standardized Streamflow index for hydrological drought using machine learning models

This article explores the suitability of a long short-term memory recurrent neural network (LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The long short-term memory works on the sequential framework which considers all of the predecessor data. This forecasting method used daily discharged data collected from the Basantapur gauging station located on the Mahanadi River basin, India. Different metrics [root-mean-square error (RMSE), Nash–Sutcliffe efficiency (ENS), correlation coefficient (R) and mean absolute error] were selected to assess the performance of the model. Additionally, recurrent neural network (RNN) model is also used to compare the adaptability of LSTM-RNN over RNN and naive method. The results conclude that the LSTM-RNN model (R = 0.943, ENS = 0.878, RMSE = 0.487) outperformed RNN model (R = 0.935, ENS = 0.843, RMSE = 0.516) and naive method (R = 0.866, ENS = 0.704, RMSE = 0.793). The finding of this research concludes that LSTM-RNN can be used as new reliable AI technique for low-flow forecasting.

Long short term memory (LSTM) recurrent neural network for low flow hydrological time series forecasting

This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Ni˜ na-like SST anomalies leading to drought in North America, and El-Ni˜ no-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Ni˜ no-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21 st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models’ ability to predict tropical SSTs. 2010 JohnWiley &Sons,Ltd.WIREs Clim Change2010 DOI:10.1002/wcc.81

Drought under Global Warming: A Review

Forecasting of water level in multiple temperate lakes using machine learning models

Hydrologic time series modeling using historical records plays a crucial role in forecasting different hydrological processes. The objective of this study is to analyze the suitability of Support Vector Regression (SVR) for modeling monthly low flows time series for three stations in Mahanadi river basin, India. The ‘low flow’ threshold was taken as the Q75 discharge, i.e., the flow is equal to or surpassed for the duration of 75% of the observation period which was obtained from the daily discharge data. The potential applicability of SVR model is assessed with two different framework models (ANN-ELM, GPR) based on various statistical measures (r2, RMSE, MAE, Nash-Sutcliffe coefficient, objective function (OBJ), Scatter Index (SI) and BIAS). The model selection was based on lowest OBJ value for each station amongst three models (SVR, ANN-ELM, GPR). The SVR model was trained using the Radial Basis Function (RBF). Using the same inputs, the other two models (ANN-ELM and GPR) was also tested. From results, among all the stations, the SVR outperformed GPR and ANN-ELM with lowest OBJ value for the three stations a (1.378, 1.202, 1.570). In addition, the accuracy of the three models were checked using mean forecasting error which were (0.474, 0.421, 0.509) for SVR, (0.507, 0.489 0.500) for GPR and (0.564, 0.603, 0.772) ANN-ELM for the three stations. The results confirm that SVR can be used satisfactorily for modeling monthly low flows in the Mahanadi river basin, India. Hence, the SVR model could be employed as a new reliable and accurate data intelligent approach for predicting the ‘low flow’ (Q75 discharge) based on precedent data in water resources and its allied field.

Application of Support Vector Regression for Modeling Low Flow Time Series

Estimating Manning’s roughness coefficient ( n ) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning’s n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio ( α ) , relative depth ( β ) , sinuosity ( s ) , Channel bed slope ( S o ) , and meander belt width ratio ( ω ) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning’s n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning’s coefficient and subsequently discharge through Manning’s equation in the compound meandering channels are found to be quite adequate.

https://www.mdpi.com/2306-5338/5/3/47/pdf

Anticipate Manning’s Coefficient in Meandering Compound Channels

Assessments of low flows of major river of India are essential for effective management of drought and irrigation water demand/supply mechanism. In the present work, bivariate low flow frequency an...

Bivariate low flow return period analysis in the Mahanadi River basin, India using copula

Low flow in the river is an important parameter that directly impacts on various water management activities. Therefore, the study of long-term low flow records is essential for managing sustainable water resources in a river basin. The impacts of climate change on water resources has shown a significant change in river discharge, making the study of low flow trends and its variability even more crucial. Trends of the various magnitude of low flows such as 1-day min flow, 3-day min flow, 7-day min flow, 30-day min flow, 90-day min flow for 14 stations over Mahanadi River basin India are studied using the Modified Mann Kendall test. Further, the multivariate Bayesian change point analysis has been carried out for detecting the significant change year. The results obtained show an increasing trend in the low flow in the upper part of the Mahanadi River basin as most of the stations. Which are showing a decreasing trend of low flow indices are present in the upper part of the basin. This study concludes the presence of a mixed low flow trend, i.e., increasing, decreasing, and some stations with no trend. From Multivariate Bayesian change point, analysis advocates that there is evidence of some significant change point in all the stations between the period 1995 and 2006. The analysis revealed that the Mahanadi River basin is highly vulnerable to low flow conditions driven by the variability in low flow discharge, which may be linked to an increase in anthropogenic activities in the basin.

Bibhuti Bhusan Sahoo

Papers

Long short term memory (LSTM) recurrent neural network for low flow hydrological time series forecasting

Application of Support Vector Regression for Modeling Low Flow Time Series

Anticipate Manning’s Coefficient in Meandering Compound Channels

Bivariate low flow return period analysis in the Mahanadi River basin, India using copula

Assessment of low flow trends and change point detection in Mahanadi River basin, India