Deep learning models comparable assessment and uncertainty analysis for diurnal temperature range (DTR) predictions over Indian urban cities

For monthly time spans of Indian cities (Ahmedabad, Bengaluru, Guwahati, Kolkata, and New Delhi), this article used daily (max and min) temperature data from 1951 to 2020 and approximated diurnal temperature range (DTR). RClimDex (a user interface for extreme computing indices) was used to do statistical analysis and comparisons of climatological characteristics such time series, means, extremes, and trends. During these years, the DTR trend in the researched area was more variable. This research examines the appropriateness of three deep neural networks [recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)] for one-step-ahead DTR time series (DTRTS) prediction. To test the efficiency of models in the testing set, six statistical error metrics were used [Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of correlation (R), Percent Bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd). The forecasting inaccuracy in predicting the outcome The Wilson score approach was used to do a quantitative uncertainty analysis on DTR. The results show that the LSTM outperforms the other two models in terms of forgetting, remembering, and updating information. It is more accurate on datasets with longer sequences and exhibits substantially higher volatility throughout its gradient descent. It got concluded with LSTM being well adapted to learning from experience to categorize, analyze, and predict time series, and it may be employed as a new dependable artificial intelligence technique for DTRTS forecasting.

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

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.

LSTM recurrent neural network for low flow Hydrological Time Series Forecasting

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 naïve 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 naïve 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.

Source characterization and human health risk assessment of nitrate in groundwater of middle Gangetic Plain, India

Nitrate (NO3) pollution is a global concern as it affects the whole ecosystem: human, livestock, economy, and environment. The elevated levels of nitrate in groundwater can directly pose risks to population. A total of 156 representative groundwater samples were collected from groundwater sources such as hand pumps and bore wells across the study area. To identify the source of nitrate with its associated attributes, multivariate statistical methods (factor analysis (FA), sparse principal component analysis (SPCA)) were used in this study. In addition, empirical Bayesian kriging (EBK) modeling was used to predict the nitrate at ungauged locations of the study area. From the analysis of results, it was found that 5% of the groundwater samples exceeded the acceptable limit (50 mg l?1) of nitrate as specified by the World Health Organization (WHO). The first principal component (PC) indicated by the SPCA was salinity factor, which was significantly contributed by electrical conductivity followed by sulfate. The fourth PC represented the nitrate as a factor and positive loading of nitrate was strongly associated with chloride, sulfate, and calcium. The associated loading of nitrate with water quality attributes indicated that elevated level of nitrate in groundwater may be due to external sources that came through anthropogenic activity. A similar conclusion was drawn from factor analysis as well, indicating that SPCA can be applied as a new method for groundwater geochemistry. Hazard index calculations showed that infants of the study region were at a higher risk compared to the adults and children.

Nitrate (NO3) pollution is a global concern as it affects the whole ecosystem: human, livestock, economy, and environment. The elevated levels of nitrate in groundwater can directly pose risks to population. A total of 156 representative groundwater samples were collected from groundwater sources such as hand pumps and bore wells across the study area. To identify the source of nitrate with its associated attributes, multivariate statistical methods (factor analysis (FA), sparse principal component analysis (SPCA)) were used in this study. In addition, empirical Bayesian kriging (EBK) modeling was used to predict the nitrate at ungauged locations of the study area. From the analysis of results, it was found that 5% of the groundwater samples exceeded the acceptable limit (50 mg l?1) of nitrate as specified by the World Health Organization (WHO). The first principal component (PC) indicated by the SPCA was salinity factor, which was significantly contributed by electrical conductivity followed by sulfate. The fourth PC represented the nitrate as a factor and positive loading of nitrate was strongly associated with chloride, sulfate, and calcium. The associated loading of nitrate with water quality attributes indicated that elevated level of nitrate in groundwater may be due to external sources that came through anthropogenic activity. A similar conclusion was drawn from factor analysis as well, indicating that SPCA can be applied as a new method for groundwater geochemistry. Hazard index calculations showed that infants of the study region were at a higher risk compared to the adults and children.