Godavari River Basin Research Articles

First Report on the Occurrence of Aberrant Extra Dorsal Fins in Ompok bimaculatus (Bloch, 1794) from Godavari River Basin, India

First Report on the Occurrence of Aberrant Extra Dorsal Fins in Ompok bimaculatus (Bloch, 1794) from Godavari River Basin, India

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

Streamflow estimation using SWAT model including large storage reservoirs in Godavari River Basin, India

Streamflow estimation using SWAT model including large storage reservoirs in Godavari River Basin, India

Joint probabilistic behaviour of climate extremes over the Godavari River basin, India

AbstractClimate change has an adverse effect on the global hydrological cycle and hence climatic extremes are becoming more persistent. Therefore, the present study analyses the characteristics of the compound extremes in India using the Godavari River basin as a test bed. The study examines the individual return period (IRP) and pairwise joint return period (JRP) of several climate extreme indices such as consecutive dry days (CDD), consecutive wet days (CWD), percentage rainfall of very dry days (R5Tot) and very hot days (TX95) considering historical and future period of two scenarios, SSP245 and SSP585, from the CMIP6 consortium. Both SSPs indicates an upward trend in the indices for CWD and TX95, whilst showcasing a declining trend for CDD and R5Tot at 5% significance level. Under SSP585, the values of JRPs , , and ranges from 3 to 5 years, 14–55 years, 35–274 years and 104 to >1000 during epoch‐1 (2023–2060). During epoch‐2 (2061–2100), the JRPs values are generally higher. Regions displaying lower JRPs values suggest a higher likelihood of simultaneous occurrences of extremes such as floods and droughts, as well as very dry days and warm days within the same year. Under SSP585, the maximum (minimum) value of change rate (CR) of JRP for and during epoch‐1 is more than 900% (−80%) and 1000% (−90%), whereas during epoch‐2, the corresponding CR values are greater than 700% (−80%) and 1000% (−90%). The future possibility of co‐occurrence of ‘CDD and CWD’ and ‘R5Tot and TX95’, both exceeding their 10‐ and 25‐year IRP, in a single year, is highly likely for the regions with low CR values and unlikely for the regions with high CR values. The study would help in understanding the spatial and temporal variability of compound climate extremes over the Godavari River basin.

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Environmental controls on the distribution of brGDGTs and brGMGTs across the Seine River basin (NW France): implications for bacterial tetraethers as a proxy for riverine runoff

Abstract. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial lipids that have been widely used as environmental proxies in continental paleorecords. Another group of related lipids, branched glycerol monoalkyl glycerol tetraethers (brGMGTs), has recently been proposed as a potential paleotemperature proxy. Nevertheless, the sources and environmental dependencies of both brGDGTs and brGMGTs along the river–sea continuum are still poorly understood, complicating their application as paleoenvironmental proxies in some aquatic settings. In this study, the sources of brGDGTs and brGMGTs and the potential factors controlling their distributions are explored across the Seine River basin (NW France), which encompasses the freshwater-to-seawater continuum. BrGDGTs and brGMGTs were analyzed in soils, suspended particulate matter (SPM), and sediments (n=237) collected along the land–sea continuum of the Seine basin. Both types of compounds (i.e., brGDGTs and brGMGTs) are shown to be produced in situ, in freshwater and saltwater, based on their high concentrations and distinct distributions in aquatic settings (SPM and sediments) vs. soils. Redundancy analysis further shows that both salinity and nitrogen dominantly control the brGDGT distributions. Furthermore, the relative abundance of 6-methyl vs. that of 5-methyl brGDGTs (the IR6Me ratio), the total nitrogen (TN), the δ15N, and the chlorophyll a concentration co-vary in a specific geographical zone with low salinity, suggesting that 6-methyl brGDGTs are preferentially produced under low-salinity and high-productivity conditions. In contrast to brGDGTs, the brGMGT distribution appears to be primarily regulated by salinity, with a distinct influence on the individual homologues. Salinity is positively correlated with homologues H1020a and H1020b and negatively correlated with compounds H1020c and H1034b in SPM. This suggests that bacteria living in freshwater preferentially produce compounds H1020c and H1034b, whereas bacteria that primarily grow in saltwater appear to be predominantly responsible for the production of homologues H1020a and H1020b. Based on the abundance ratio of the freshwater-derived compounds (H1020c and H1034b) vs. their saltwater-derived homologues (H1020a and H1020b), a novel proxy, the Riverine IndeX (RIX), is proposed to trace riverine organic matter inputs, with high values (>0.5) indicating a higher riverine contribution. We successfully applied RIX to the Godavari River basin (India) and a paleorecord across the upper Paleocene and lower Eocene from the Arctic Coring Expedition at Lomonosov Ridge, showing its potential applicability to both modern samples and paleorecords.

Assessment of Hydrological Changes in Godavari River Basin Under the Impacts of El-Niño

Assessment of Hydrological Changes in Godavari River Basin Under the Impacts of El-Niño

Assessing flood susceptibility in the Godavari River Basin, Bhadrachalam Region, Sothern India: A GIS-AHP Multi-Criteria Approach

Floods, as recurrent natural disasters, exert significant impacts on both the environment and human settlements. This study focuses on evaluating the flood susceptibility of the Bhadradri Kothagudem district in Telangana, situated within the Godavari River Basin. The research integrates Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) in a multi-criteria approach to analyze and map flood susceptibility. Landsat-8 data, digital elevation model data and rainfall serve as inputs for assessing the flood susceptibility. The study considers various topographical features such as elevation, slope, roughness, contours and aspect, along with factors like land use land cover, flow accumulation, stream direction, stream network, drainage density, flow length, distance from the river, soil, normalized difference vegetation index and topographic wetness index. These variables are rescaled on a scale of one to five and combined to generate a comprehensive flood susceptibility map of the Kothagudem district using GIS. The AHP is implemented through GIS, assigning weightages on a scale of one to five based on the priority of spatial classes within thematic maps. The flood susceptibility map is produced on a scale of five, designating scale class five as of very high susceptibility and scale class one as low. Scale classes two, three and four represent intermediate levels of susceptibility. The resulting flood susceptibility maps offer valuable insights for disaster preparedness, risk mitigation and land-use planning in the Kothagudem district. The integration of GIS and AHP provides a robust methodology for assessing and visualizing flood susceptibility, enabling informed decision-making for resilient and sustainable development in flood-prone regions.

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071–2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.

A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

The importance of water quality models has increased as their inputs are critical to the development of risk assessment framework for environmental management and monitoring of rivers. However, with the advent of a plethora of recent advances in ML algorithms better predictions are possible. This study proposes a causal and effect model by considering climatological such as temperature and precipitation along with geospatial information related to the agricultural land use factor (ALUF), the forest land use factor (FLUF), the grassland usage factor (GLUF), the shrub land use factor (SLUF), and the urban land use factor (ULUF). All these factors are included in the input data, whereas four Stream Water Quality parameters (SWQPs) such as Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, and Dissolved Oxygen (DO) from 2019 to 2021 are taken as outputs to predict the Godavari River Basin water quality. In the preliminary investigation, out of these four SWQPs, nitrate's coefficient of variation (CV) is high, revealing a close association with climate parameters and land use practices across the sampling stations. In the authors' earlier study, a model using a single-layer Feed-Forward Neural Network (FFNN) showed improved performance in predicting cause and effect factors linked to water quality metrics. To achieve better prediction, a stacked ANN meta-model and nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive Boosting (ADB), Decision Tree (DT), Highest Gradient Boosting (HGB), Light Gradient Boosting Method (LGBM), and Gradient Boosting (GB), were compared in this study. According to the study's findings, Bagging and Boosting models outperformed stand-alone earlier FFNN for the same dataset and showed superior predictive capabilities in terms of accuracy in forecasting the variable of interest. For instance, during testing, the coefficient of determination (R2) of Biochemical Oxygen Demand (BOD) increased from 0.72 to 0.87. Furthermore, a stacked Artificial Neural Network (ANN) meta model that was reinforced using Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Trees (ET) as base models performed better than the individual ML models (from R2 = 0.87 to 0.91 for BOD in testing). By using this new framework, the effort for hyperparameter tuning can be minimized.

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Enhanced morphometric analysis for soil erosion susceptibility mapping in the Godavari river basin, India: leveraging Google Earth Engine and principal component analysis

ABSTRACT Morphometric analysis of a drainage basin along with its stream network is indispensable for analysing a river basin’s prevailing geomorphology, geo-hydrology, and erosional susceptibility. In this study, an enhanced morphometric evaluation method using Google Earth Engine (GEE) and Principal Component Analysis (PCA) was used to assess and prioritize 12 sub-basins of the Godavari River basin (GRB), India, based on their erosional susceptibility. The findings of erosional susceptibility were validated using the Sediment Yield (SY) model. Our findings highlight that the sub-basins of Indravati, Pranhita, and Manjira, i.e. 42.42% (132707.4 km2) of the entire GRB are at high risk of erosional susceptibility, requiring immediate implementation of soil conservation measures. Our findings also highlight that 52.71% of the GRB is in the mature stage of geological evolution, wherein erosional landform-building processes are still ongoing. Overall, GRB has a relatively low drainage density with dendritic drainage patterns, permeable soils, and elongated sub-basins of enhanced erosional susceptibility. Moreover, the results of the SY model exhibited 90% agreement with the results of the enhanced morphometric analysis. This study presents an efficient framework for enhanced morphometric analysis that can assist policymakers in the demarcation of priority zones for erosional susceptibility.

A novel partitioning of gross primary production and water use efficiency for sustaining water and food security using Budyko hypothesis

This study coupled the green water and blue water accounting with the existing standard Budyko framework and Fu's 1-parameter Budyko framework to diagnose the basin hydrological behavior. Both Budyko frameworks were employed to determine green water consumption (ETGreen) and blue water consumption (ETBlue) which, in turn, were used to map the blue water index (BWI) hotspots and green water index (GWI) bright spots. The relative contributions of green water and blue water were quantified for sustaining water and food security. A new methodology is proposed using BWI and GWI for partitioning the Gross Primary Production (GPP) and Water Use Efficiency (WUE) into GPPBlue, GPPGreen and WUEBlue and WUEGreen. The methodology was applied to five sub-basins of the Central Godavari River Basin (CGRB): Purna, Dhalegaon, GR Bridge, Yeli and Delta. Results showed that all five basins exhibited larger deviations from the theoretical Budyko curve of Fu's 1-parameter Budyko framework than did the standard Budyko framework and the Dhalegaon basin showed the largest deviations. The partitioning of GPP and WUE by the proposed methodology showed that the proportion of GPPGreen to the total GPP was much higher than that of the GPPBlue. Similarly, the proportion of WUEGreen to WUE was more than that of WUEBlue. The mapping of GPPBlue and GPPGreen, and WUEBlue and WUEGreen showed that the Delta and Yeli basins had the highest values of both GPPGreen & GPPBlue and WUEBlue and WUEGreen (bright spot basins) and the Dhalegaon and parts of GR Bridge basin had the lowest values (hot spot basins). The proposed partitioning of GPP and WUE will help identify the relative contributions of green water and blue water (for managing agricultural waters) and formulate agronomical and engineering practices for stakeholders and policy makers for increasing the overall WUE and GPP to sustain water and food security.

Mitigating El Niño impacts on hydro-energy vulnerability through identifying resilient run-of-river small hydropower sites

Study regionThe Godavari River basin (GRB), situated between the geographical coordinates of 73°21′ E to 83°09′ E and 16°07′ N to 22°50′ N, India Study focusWe developed an integrated framework combining the variable infiltration capacity (VIC) model and geospatial tools to identify potential sites for run-of-river small hydropower plants (RoR-SHP) capable of meeting the GRB's energy demands during El Niño events. This study utilized long-term (1951–2020) daily streamflow data, simulated with the VIC model for design discharge computation at 30%, 75%, and 90% flow dependability and provided a thorough assessment of the potential of RoR-SHP in the GRB. New hydrological insights for the regionThe analysis revealed the GRB's potential for RoR-SHP development, identifying 226 potential sites based on the head along the river, with a combined power and annual energy generation estimate of 92 MW and 0.4 TWh/yr, respectively, at 90% flow dependability. After meticulous screening, 11 potential sites based on the head and the power potential demonstrated a decline of 46.03%, 37.97%, and 17.77% in annual energy at 30%, 75%, and 90% flow dependability, respectively, leaving nine sites maintaining the firm power (90% flow dependability) even during El Niño years. Our findings underscore the increased risk of power shortages during El Niño years and emphasize the need to develop appropriate strategies to cope with the risks associated with El Niño events.

IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India

Addressing a pressing need for streamlined access to observed meteorological data in India, we present IMDLIB, an open-source Python library that operates on gridded datasets from the India Meteorological Department (IMD). IMDLIB simplifies the retrieval, management, and processing of IMD's rainfall, maximum and minimum temperature data at varying scales (point, regional, catchment, and country scales). IMDLIB stands out by directly interfacing with IMD's database through robust APIs, streamlining data acquisition. It offers transformation of IMD's binary data into common formats (e.g., NetCDF/GeoTIFF/CSV) and advanced analytics, including trend analysis, spatiotemporal data interpolation, calculation of meteorological indices and extreme characteristics, showcasing superior capabilities compared to traditional meteorological data processing tool available for the region. To underline its robustness, IMDLIB's application was illustrated in two distinct regions: Rajasthan State and the Godavari River Basin. Through IMDLIB, we envision enhanced data accessibility, leading to more insightful hydro-climatic research and analysis.

Investigating river water quality assessment through non‐parametric analysis: A case study of the Godavari River in India

AbstractThis study examines temporal variations in water quality parameters of Godavari River. The present study examines the concentrations of seven water quality parameters, namely total alkalinity (Alk), calcium (Ca), chlorine (Cl), total hardness (Hard), magnesium (Mg), sodium (Na), and sulfate (SO4), using data from 1981 to 2005 at seven gauging stations, that is, Mancherial, Pathagudem, Nowrangpur, Jagdalpur, Perur, Konta, and Polavaram. A seasonal analysis (pre‐monsoon, monsoon, and post‐monsoon) is conducted to detect trends in water quality parameters. The study also uses normality tests Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W) which reveals nonnormal distribution in the majority of datasets, necessitating non‐parametric analysis. The concentration of Alk, Ca, and Hard is found to be significantly increasing trend at some stations during all three seasons, along with significantly decreasing trend found in the concentrations of Cl, Na, and SO4 using non‐parametric Mann–Kendall's (M–K) and Sen's Slope test. The concentration of Mg depicts mixed trends at different stations which may be due the variation in agricultural practices or industrial activities surrounding the stations. It is evident that none of the parameter exhibits statistically significant trends across all seasons at any station. The results of Sen's Slope test are found to be in good agreement with Mann–Kendall's test statistics. A second order auto‐regressive model (AR (2)) is successfully employed to predict and forecast the water quality parameters in present and future time steps. The authenticity of AR (2) model is assessed using Nash‐Sutcliffe (NSE) efficiency and coefficient of determination (R2). The values of NSE and R2 are found to be more than 0.85 for all parameters during all the three seasons, along with, the values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results of coefficient of variation (CV) and persistence‐criterion (PC) also depicts that AR (2) can successfully be utilized in forecasting the water quality parameters. The research contributes insights for effective water resource management in Godavari River basin.

Impacts of climate change on drought and its consequences on the agricultural crop under worst-case scenario over the Godavari River Basin, India

Extreme dry and hot conditions lead to intense droughts in central India. However, the precise changes in future drought characteristics and their consequences on crop production have not been fully examined in Godavari River Basin. Therefore, this study focuses on the spatiotemporal modelling of climate change impacts on drought over the past, present, and future periods using long-term high spatial gridded data. The past and present observed datasets were obtained from Indian Meteorological Department, whereas the future datasets were obtained from d4PDF Scenarios. The standardized precipitation evapotranspiration index (SPEI) was applied to analyse drought statistics to identify the worst-case drought scenario (i.e. year most affected by severe-to-extreme drought with high drought severity), which was used further to access precise drought risk. Because SPEI considers the effect of both rainfall and temperature and therefore it is the widely recommended meteorological drought indices and best suited for climate change impact assessment. Drought severity was analysed using a Multi-criteria assessment risk matrix, and the areas under higher agriculture drought risk were mapped. The frequency of droughts with higher intensity and magnitude increased in the present decade compared to the past three decades. 75% area showed higher vulnerability to drought risks, whereas only 25% showed lower risks and less vulnerability to drought occurrences. Results demonstrate that drought severity varies from one sub-basin to another. More drought episodes were predicted for the central belt, particularly over the sub-basins of Wardha, Wainganga, Pranitha, and part of Indravati and Lower Godavari. 11% of the area covering six districts in the GRB was identified as highly vulnerable, and rice production was drastically reduced, accounting for 41.02% of production loss during the worst-case drought event. The frequency of severe-to-extreme droughts is expected to increase under future scenarios; therefore, effective mitigation strategies are recommended to minimise the agriculture drought risks and economic loss.

Heavy Metal Analysis in Agricultural Soils in Godavari River Basin of Rajahmundry Region, East Godavari District, Andhra Pradesh, India.

There are many sectors that are using chemicals and increase their production to meet the needs of people in the world. The domestic, industrial, agriculture, medical and recent technological applications effluents consist of heavy metals that have high density than water and are raising the concerns of potential effects on the environment and also human. The soil samples are collected from Godavari river basin near Rajahmundry in East Godavari district, Andhra Pradesh, to assess the levels of Ni, Fe, Cr, Cu, Zn, Co, Pb, Cd & As to calculate soil PERI. Pearson’s correlation matrix, Contamination factor, Coefficient variations are used for evaluate contamination of pollutants & ecological risk, based on results the accumulation efficiency is very different in soils; most of the samples have high concentration. Contamination factors of heavy metals are in moderate contamination (1 ≤ 3), but the Cd shows as considerable contamination (3 ≤ 5) in Aalamuru-S5, Korumilli -S7 and Kulla-S8 soils samples. The coefficient variation was observed as (CV > 0.36) high variation in heavy metal (Cd > Cu > As > Fe > Co > Zn > Pb > Ni > Cr). Based on results the toxic concentrations are gradually increases in the soil samples due to over usage of chemical fertilizers and may be accumulated into crops, leads to ecological risk, finally agricultural soil are contaminating by heavy metals in our study area.

Multivariate multi-step LSTM model for flood runoff prediction: a case study on the Godavari River Basin in India

Abstract Flood is India's most prevalent natural calamity, devastatingly affecting human lives, infrastructure, and agriculture. Predicting floods can help to mitigate the potential damage and conduct timely evacuation drives. This research proposes a deep-learning regression model to forecast flood runoff. Various climatological, hydrological, land, and vegetation-related data have been collected from multiple sources for 18 years (2002–2019) to create a comprehensive dataset for the Godavari River at the Perur water station in India. The relevant attributes identified through feature selection are river water level, precipitation, temperature, surface pressure, evaporation, soil water content, daily runoff, and average river flow. The selected features were fed into various time series prediction models like AutoRegressive Integrated Moving Average (ARIMA), Prophet, Neural Prophet, and Long Short-Term Memory (LSTM). The LSTM model obtained the best results achieving a Root Mean Squared Error (RMSE) value of 0.05, Mean Absolute Error (MAE) value of 0.007, Willmott's Index (WI) of 0.83, Legates-McCabe's Index (LMI) of 0.58, and R2 of 0.67 for a 1-day prediction with a look-back window of 183 days. The model is also trained to predict the flood runoff value for a week ahead. The proposed model can serve as an essential component in flood warning systems.