AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

Support vector machine based prediction of photovoltaic module and power station parameters

ABSTRACT

The uncertainty in the output power of the photovoltaic (PV) power generation station due to variation in meteorological parameters is of serious concern. An accurate output power prediction of a PV system helps in better design and planning. The present study is carried out for the prediction of output power of PV generating station by using Support Vector Machines. Two cases are considered in the present study for prediction. Case-I deals with the prediction of PV module parameters such as V_oc, I_sh, R_s, R_sh, I_max, V_max, P_max, and case-II deals with the prediction of power generation parameters such as P_DC, P_AC, and system efficiency. Historical data of PV power station with an installed capacity of 10 MW and weather information are used as input to develop four different seasons-based SVM models for all parameters. The performance results of the models are presented in terms of Mean Relative Error (MRE) and Root Mean Square Error (RMSE). Additionally, the performance results obtained with polynomial and Radial Based Function kernel are also compared to show that which kernel has better prediction accuracy, and practicability. The result shows that the minimum average RMSE and MRE for case-I with Radial Based Function kernel are 0.034%, 0.055%, 0.002%, 1.726%, 0.044%, 0.047%, 2.342%, and 0.005%, 0.014%, 0.079%, 0.885%, 0.005%, 0.007%, 0.013%, and for case-II with poly kernel are 0.014%, 0.016%, 0.149% and 0.011%, 0.0175, 1.03%, respectively. The present study will be helpful to provide technical guidance to the prediction of the PV power System.     

Prediction of global solar radiation using support vector machines

This article utilizes Support Vector Machines (SVM) for predicting global solar radiation (GSR) for Sharurha, a city in the southwest of Saudi Arabia. The SVM model was trained using measured air temperature and relative humidity. Measured data of 1812 values for the period from 1998–2002 were obtained. The measurement data of 1600 were used for training the SVM, and the remaining 212 were used for comparison between the measured and predicted values of GSR. The GSR values were predicted using the following four combinations of data sets: (i) Daily mean air temperature and day of the year as inputs, and global solar radiation as output; (ii) daily maximum air temperature and day of the year as inputs, and GSR as output; (iii) daily mean air temperature and relative humidity and day of the year as inputs, and GSR as output; and (iv) daily mean air temperature, day of the year, relative humidity, and previous day’s GSR as inputs, and GSR as output. The mean square error was found to be 0.0027, 0.0023, 0.0021, and 7.65 × 10^?4 for case (i), (ii,), (iii), and (iv) respectively, while the corresponding absolute mean percentage errors were 5.64, 5.08, 4.48, and 2.8%. Obtained results show that the SVM method is capable of predicting GSR from measured values of temperature and relative humidity.     

OPTICAL HETEROGENEITY IN GREEN BAY1

Differences in light penetration and light attenuating components and processes are documented along 112 km of the major (NE/SW) axis of Green Bay (Lake Michigan) during a three-day cruise (May 25–27, 1982). Measurements included diffuse attenuation of downwelling irradiance (k_d), Secchi disk transparency (SD), phytoplankton pigments (chlorophyll a and phaeophytin), turbidity (T_n), and dissolved color (absorbance). The relative importance of absorption and scattering to attenuation was calculated from paired measurements of k_d and SD. Absorption and scattering coefficients were calculated; value estimates were supported by a strong linear relationship between the scattering coefficient (b) and T_n (b = 0.99 *T_n; r²= 097, n = 12). Light attenuation characteristics, including the extent of light penetration and the magnitudes and relative importance of absorption, scattering and individual attenuating components, were found to be heterogenous in space. This heterogeneity is due to the characteristics and positions of entry of fluvial discharges to the bay as they influence levels of dissolved color (Gelbstoff), phytoplankton standing crop, and inorganic particulates. Identification of key processes regulating light penetration and their potential for response to pollution control measures can aid in the development of a water quality management plan for Green Bay.     

Climatic Controls on Watershed Reference Evapotranspiration Varied during 1961–2012 in Southern China

Reference evapotranspiration (ET_o) is an important hydrometeorological term widely used in understanding and projecting the hydrological effects of future climate and land use change. We conducted a case study in the Qinhuai River Basin that is dominated by a humid subtropical climate and mixed land uses in southern China. Long‐term (1961–2012) meteorological data were used to estimate ET_o by the FAO‐56 Penman–Monteith model. The individual contribution from each meteorological variable to the trend of ET_o was quantified. We found basin‐wide annual ET_o decreased significantly (p < 0.05) by 3.82 mm/yr during 1961–1987, due to decreased wind speed, solar radiation, vapor pressure deficit (VPD), and increased relative humidity (RH). However, due to the increased VPD and decreased RH, the ET_o increased significantly (p < 0.05) in spring, autumn, and annually at a rate of 2.55, 0.56, and 3.16 mm/yr during 1988–2012, respectively. The aerodynamic term was a dominant factor controlling ET_o variation in both two periods. We concluded the key climatic controls on ET_o have shifted as a result of global climate change during 1961–2012. The atmospheric demand, instead of air temperature alone, was a major control on ET_o. Models for accurately predicting ET_o and hydrological change under a changing climate must include VPD in the study region. The shifts of climatic control on the hydrological cycles should be considered in future water resource management in humid regions.     

STREAM TEMPERATURE ESTIMATION FROM AIR TEMPERATURE1

ABSTRACT: Air temperatures are sometimes used as substitutes for stream temperatures. To examine the errors associated with this procedure, linear relationships between stream temperatures, T, and air temperatures, T_a, recorded for 11 streams in the central U.S. (Mississippi River basin) were analyzed. Weather stations were an average 42 miles (range 0 to 144 miles) from the rivers. The general equations, T_w= 5.0 + 0.75 T_a and T_w= 2.9 + 0.86 T_a with temperatures in °C, were derived for daily and weekly water temperatures, respectively, for the 11 streams studied. The simulations had a standard deviation between measurements and predictions of 2.7°C (daily) and 2.1°C (weekly). Equations derived for each specific stream individually gave lower standard deviations, i.e., 2.1°C and 1.4°C, respectively. Small, shallow streams had smaller deviations than large, deep rivers. The measured water temperatures follow the air temperatures closely with some time lag. time lags ranged from hours to days, increasing with stream depth. Taking into account these time lags improved the daily temperature predictions slightly. Periods of ice cover were excluded from the analysis.     

ESTIMATION OF VOLUME-DURATION-FREQUENCY RELATIONS OF UNGAGED SMALL URBAN STREAMS IN OHIO1

ABSTRACT: This paper describes methods for estimating volume-duration-frequency relations of urban streams in Ohio with drainage areas less than 6.5 square miles. The methods were developed to assist engineers in the design of hydraulic structures on urban streams for which temporary storage of water is an important element of the design criteria. Multiple-regression equations were developed for estimating maximum flood volumes of d-hour duration and T-year recurrence interval (dV_T). Maximum annual flood-volume data for all combinations of six durations (1, 2, 4, 8, 16, and 32 hours) and six recurrence intervals (2, 5, 10, 25, 50, and 100 years) were analyzed. The significant explanatory variables in the resulting 36 volume-duration-frequency equations are drainage area, average annual precipitation, and basin-development factor. Standard errors of prediction for the 36 dV_T equations range from ±28 percent to ±44 percent.     

ESTIMATING REFERENCE EVAPORATION IN THE CENTRAL VALLEY OF CALIFORNIA USING THE LINACRE MODEL1

ABSTRACT: The Linacre (1988) model for calculating evaporation from open water or well-watered surfaces only requires inputs of air temperature, latitude and elevation, and windspeed if it is available. The model was developed using data collected at a large number of sites in different climatic regions of the world, while independent tests of the model have shown it to be suitable for estimating evaporation in a variety of locations. This study was intended to contribute to the broad goal of evaluating temperature-based evaporation models for use in California by testing the Linacre model in the agriculturally intensive Central Valley. Observed monthly mean reference evaporation (E_o) and meteorological data for periods ranging up to 72 months were obtained from 25 California Irrigation and Management Information System (CIMIS) stations distributed throughout the Central Valley. Uncalibrated and calibrated Linacre models were used to estimate monthly mean reference evaporation, and the performance of each model was evaluated using indices that quantified the random and systematic errors and overall model performance. The accuracy of the radiation and ventilation components of the model were evaluated separately. The uncalibrated model was found to systematically overestimate E_o with most of the model error being attributed to the ventilation component. Calibration of the radiation and ventilation components removed most of the systematic model errors, and the root mean square error for monthly mean E_o was 0.676 mm day^?1 (16.8 percent of the mean observed value). (KEY TERMS: reference evaporation; Linacre model; irrigation scheduling.)     

Changing effluent chemistry affect survival,growth and physiological function of <Emphasis Type="Italic">Acacia nilotica</Emphasis> seedlings in northwestern region of India

Recycling and conservation efforts for water are the need of the day because of the lack of new water sources and the ever-increasing demand for drinking water. Seedlings of Acacia nilotica L. were irrigated with: canal water (T₁, control); municipal effluent (T₂); textile effluent (T₃); steel effluent (T₄); textile + municipal effluent in 1:1 ratio (T₅); steel + municipal effluent in 1:2 ratio (T₆); steel + textile in 1:2 ratio (T₈) and steel + municipal + textile in 1:2:2 ratio (T₇) with views to observe effluents effect on the seedlings and its adaptability and to recommend safe disposal of these effluents. Seedlings in T₆, T₇ and T₈ showed 50% lesser height and collar diameter than those in control. Seedlings in T₂ attained greatest height, collar diameter, numbers of branches and produced 140 g dry biomass seedling⁻¹. Highest concentration of manganese (Mn), iron (Fe), copper (Cu) and zinc (Zn) and lowest concentration of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) in the seedlings of T₄, T₆, T₇ and T₈ resulted in nutritional imbalance, mineral toxicity and reduction in photosynthetic (Pn) and transpiration (E) rates and caused seedling mortality. Seedlings of T₃ had highest sodium concentration and low concentration of Ca, Mg and micronutrients resulting in nutritional imbalance, augmented chlorosis and reduced gas exchange and biomass by half as compared to control. Increased growth, Pn and E and biomass in seedlings of T₅ over T₃ and survival period in T₆, T₇ and T₈ seedlings suggested a beneficial effect of effluents mixing. Unscientific disposal should be avoided and toxic concentration of metal ions␣may be reduced for long-term application and harmless disposal of effluents in afforestation and urban development.     

Model simulations of dissolved oxygen characteristics of Minnesota lakes: Past and future

A deterministic, one-dimensional, unsteady numerical model has been developed, tested, and applied to simulate mean daily dissolved oxygen (DO) characteristics in 27 lake classes in the state of Minnesota. Reaeration and photosynthesis are the oxygen sources, while respiration, sedimentary, and biochemical water column oxygen demand are the sinks of oxygen in the model. The lake classes are differentiated by surface area (A _s), maximum depth (H _max), and trophic status expressed as Secchi depth (Z _s). Because lake stratification is most important to lake oxygen dynamics, simulated DO characteristics are plotted in terms of a stratification parameterA _s/H _max ^0.25 and Secchi depthZ _s. Simulations provide DO profiles on a daily time scale. Specific DO characteristics of ecological and environmental interest are epilimnetic DO, hypolimnetic DO, DO gradient from surface to bottom, and DO minima and maxima. Specific results are as follows: Simulated mean daily and weekly DO values in the epilimnion of all lakes for both past and future climate scenarios are near saturation over the summer season. Hypolimnetic DO values depend strongly on lake morphometry, trophic status, and time throughout the summer season. Future climate conditions are specified as the historical records from 1955 to 1979, adjusted (monthly) by the 2 × CO₂ GISS model output to account for doubling of atmospheric CO₂. With this climate change, weekly averaged epilimnetic DO is projected to drop by less than 2 mg/liter, and will remain above 7 mg/liter throughout the open water season. The hypolimnetic DO reductions after climate change are on the order of 2–8 mg/liter. Periods of anoxia are longer by as much as 80 days. Those changes would alter water quality dynamics in lakes and have a profound effect on lake ecosystems including indigenous fishes. The results presented are useful for evaluating environmental management options.     

Passive aeration composting of chicken litter: Effects of aeration pipe orientation and perforation size on losses of compost elements

A passive aeration composting study was undertaken to investigate the effects of aeration pipe orientation (PO) and perforation size (PS) on some physico-chemical properties of chicken litter (chicken manure + sawdust) during composting. The experimental set up was a two-factor completely randomised block design with two pipe orientations: horizontal (Ho) and vertical (Ve), and three perforation sizes: 15, 25 and 35 mm diameter. The properties monitored during composting were pile temperature, moisture content (MC), pH, electrical conductivity (EC), total carbon (C_T), total nitrogen (N_T) and total phosphorus (P_T). Moisture level in the piles was periodically replenished to 60% for efficient microbial activities. The results of the study showed that optimum composting conditions (thermophilic temperatures and sanitation requirements) were attained in all the piles. During composting, both PO and PS significantly affected pile temperature, moisture level, pH, C_T loss and P_T gain. EC was only affected by PO while N_T was affected by PS. Neither PO nor PS had a significant effect on the C:N ratio. A vertical pipe was effective for uniform air distribution, hence, uniform composting rate within the composting pile. The final values showed that PO of Ve and PS of 35 mm diameter resulted in the least loss in N_T. The PO of Ho was as effective as Ve in the conservation of C_T and P_T. Similarly, the three PSs were equally effective in the conservation of C_T and P_T. In conclusion, the combined effects of PO and PS showed that treatments Ve35 and Ve15 were the most effective in minimizing N_T loss.     

Evaluation of various global solar radiation models for Nigeria

This study assessed the performance of six solar radiation models. The objective was to determine the most accurate model for estimating global solar radiation on a horizontal surface in Nigeria. Twenty-two years meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration for the three regions, covering the entire climatic zones in Nigeria were utilized for calibrating and validating the selected models for Nigeria. The accuracy and applicability of various models were determined for three locations (Abuja, Benin City, and Sokoto), which spread across Nigeria using seven viable statistical indices. This study found that the estimation results of considered models are statistically significant at the 95% confidence level, but their accuracy varies from one location to another. However, the multivariable regression relationship deduced in terms of sunshine ratio, air temperature ratio, maximum air temperature, and cloudiness performs better than other relationships. The multivariable relationship has the least root mean square error and mean absolute bias error, not exceeding 1.0854 and 0.8160 MJ m^?2 day^?1, respectively, and monthly relative percentage error in the range of ± 12% for the study areas.     

Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye,Turkey

This paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m² respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m²/day by June to 19.19 MJ/m²/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m²/day by October to 7.20 MJ/m²/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m²/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.     

Effects of Land Use and Climate Change on Stream Temperature I: Daily Flow and Stream Temperature Projections

Freshwater mussels (order Unionida) are a highly imperiled group of organisms that are at risk from rising stream temperatures (T). There is a need to understand the potential effects of land use (LU) and climate change (CC) on stream T and have a measure of uncertainty. We used available downscaled climate projections and LU change simulations to simulate the potential effects on average daily stream T from 2020 to 2060. Monte Carlo simulations were run, and a novel technique to analyze results was used to assess changes in hydrologic and stream T response. Simulations of daily mean T were used as input to our stochastic hourly T model. CC effects were on average two orders of magnitude greater than LU impacts on mean daily stream T. LU change affected stream T primarily in headwater streams, on average up to 2.1°C over short durations, and projected CC affected stream T, on average 2.1‐3.3°C by 2060. Daily mean flow and T ratios from Monte Carlo simulations indicated greater variance in the response of streamflow (up to 55%) to LU change than in the response of stream T (up to 9%), and greater variance in headwater stream segments compared to higher order stream segments for both streamflow and T response. Simulations indicated that combined effects of climate and LU change were not additive, suggesting a complex interaction and that forecasting long‐term stream T response requires simulating CC and LU change simultaneously.     

Nutrient Bioassays of Growth Parameters for Algae in the North Bosque River of Central Texas1

Abstract: Nutrient dose‐response bioassays were conducted using water from three sites along the North Bosque River. These bioassays provided support data for refinement of the Soil and Water Assessment Tool (SWAT) model used in the development of two phosphorus TMDLs for the North Bosque River. Test organisms were native phytoplanktonic algae and stock cultured Pseudokirchneriella subcapitata (Korshikov) Hindak. Growth was measured daily by in vivo fluorescence. Algal growth parameters for maximum growth (μ_max) and half‐saturation constants for nitrogen (K_N) or phosphorus (K_P) were determined by fitting maximum growth rates associated with each dose level to a Monod growth rate function. Growth parameters of native algae were compared between locations and to growth parameters of P. subcapitata and literature values. No significant differences in half‐saturation constants were indicated within nutrient treatment for site or algal type. Geometric mean K_N was 32 μg/l and for K_P 7 μg/l. A significant difference was detected in maximum growth rates between algae types but not between sites or nutrient treatments. Mean μ_max was 1.5/day for native algae and 1.2/day for stock algae. These results indicate that watershed‐specific maximum growth rates may need to be considered when modeling algal growth dynamics with regard to nutrients.     

CLIMATE FACTOR FOR SMALL-BASIN FLOOD FREQUENCY1

ABSTRACT: A climate factor, C_T, (T = 2–, 25-, and 100-year recurrence intervals) that delineates regional trends in small-basin flood frequency was derived using data from 71 long-term rainfall record sites. Values of C_T at these sites were developed by a regression analysis that related rainfall-runoff model estimates of T-year floods to a sample set of 50 model calibrations. C_T was regionalized via kriging to develop maps depicting its geographic variation for a large part of the United States east of the 105th meridian. Kriged estimates of C_T and basin-runoff characteristics were used to compute regionalized T-year floods for 200 small drainage basins. Observed T-year flood estimates also were developed for these sites. Regionalized floods are shown to account for a large percentage of the variability in observed flood estimates with coefficients of determination ranging from 0.89 for 2-year floods to 0.82 for 100-year floods. The relative importance of the factors comprising regionalized flood estimates is evaluated in terms of scale (size of drainage area), basin-runoff characteristics (rainfall. runoff model parameters), and climate (C_T).     

Streamflow Forecasting Using Singular Value Decomposition and Support Vector Machine for the Upper Rio Grande River Basin

The current study improves streamflow forecast lead‐time by coupling climate information in a data‐driven modeling framework. The spatial–temporal correlation between streamflow and oceanic–atmospheric variability represented by sea surface temperature (SST), 500‐mbar geopotential height (Z₅₀₀), 500‐mbar specific humidity (SH₅₀₀), and 500‐mbar east–west wind (U₅₀₀) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). SVD significant regions are weighted using a nonparametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed model for the period of 1965–2014. The April–August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1–13 months. SVD results showed the streamflow variability was better explained by SST and U₅₀₀ as compared to Z₅₀₀ and SH₅₀₀. The SVM model showed satisfactory forecasting ability with best results achieved using a one‐month lead to forecast the following four‐month period. Overall, the SVM results showed excellent predictive ability with average correlation coefficient of 0.89 and Nash–Sutcliffe efficiency of 0.79. This study contributes toward identifying new SVD significant regions and improving streamflow forecast lead‐time of the URGRB.     

Production of vermifertilizer from guar gum industrial wastes by using composting earthworm <Emphasis Type="Italic">Perionyx sansibaricus</Emphasis> (Perrier)

Efforts have been made to convert the guar gum industrial waste into a value-added product, by employing a new earthworm species for vermicomposting e.g. Perionyx sansibaricus (Perrier) (Megascolecidae), under laboratory conditions. Industrial lignocellulosic waste was amended with other organic supplements (saw dust and cow dung); and three types of vermibeds were prepared: guar gum industrial waste + cow dung + saw dust in 40: 30: 30 ratio (T₁), guar gum industrial waste + cow dung + saw dust in 60: 20: 20 ratio (T_2,), and guar gum industrial waste + cow dung + saw dust in 75: 15: 10 ratio (T₃). As compared to initial concentrations, vermicomposts exhibited a decrease in organic C content (5.0–11.3%) and C:N ratio (11.1–24.4%) and an increase in total N (18.4–22.8%), available P (39.7–92.4%), and exchangeable K (9.4–19.7%) contents, after 150 days of vermicomposting. A vermicomposting coefficient (VC) was used to compare of vermicomposting with the experimental control (composting). P. sansibaricus exhibited maximum value of mean individual live weight (742.8 ± 21.1 mg), biomass gain (442.94 ± 21.8 mg), growth rate (2.95 ± 0.15 mg day⁻¹), cocoon numbers (96.0 ± 5.1) and reproduction rate (cocoons worm⁻¹ day⁻¹) (0.034 ± 0.001) in T₂ treatment. In T₃ maximum mortality (30.0 ± 4.01 %) in earthworm population was observed. Overall, T₂ vermibed appeared as an ideal substrate to manage guar gum industrial waste effectively. Vermicomposting can be proposed as a low-input basis technology to convert industrial waste into value-added biofertilizer.