Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
11 results
Search Results
Item Efficient path finding algorithm for transmission of data in agricultural field using wireless sensor network(Springer Verlag service@springer.de, 2013) Pai, S.N.; Shet, K.C.; Mruthyunjaya, H.S.Transmission of data from the source to the sink requires an efficient path so that the network life is enhanced. Longer shelf life for battery is essential when it is difficult to replace the battery too often as in agricultural field. To achieve this, in this paper we have proposed an algorithm to find the efficient path between the sink (base station) and the source so that the network can be alive for one cropping season. © 2013 Springer-Verlag.Item Fenton's treatment of actual agriculture runoff water containing herbicides(IWA Publishing 12 Caxton Street London SW1H 0QS, 2017) Sangami, S.; Manu, B.This research was to study the efficiency of the Fenton's treatment process for the removal of three herbicides, namely 2,4-dichlorophenoxy acetic acid (2,4-D), ametryn and dicamba from the sugarcane field runoff water. The treatment process was designed with the Taguchi approach by varying the four factors such as H 2 O 2 /COD (1-3.5), H 2 O 2 /Fe 2+ (5-50), pH (2-5) and reaction time (30-240 min) as independent variables. Influence of these parameters on chemical oxygen demand (COD), ametryn, dicamba and 2,4-D removal efficiencies (dependent variables) were investigated by performing signal to noise ratio and other statistical analysis. The optimum conditions were found to be H 2 O 2 /COD: 2.125, H 2 O 2 /Fe 2+ : 27.5, pH: 3.5 and reaction time of 135 min for removal efficiencies of 100% for ametryn, 95.42% for dicamba, 88.2% for 2,4-D and with 75% of overall COD removal efficiencies. However, the percentage contribution of H 2 O 2 /COD ratio was observed to be significant among all four independent variables and were 44.16%, 67.57%, 51.85% and 50.66% for %COD, ametryn, dicamba and 2,4-D removal efficiencies, respectively. The maximum removal of herbicides was observed with the H 2 O 2 dosage of 5.44 mM and Fe 2+ dosage of 0.12 mM at pH 3.5. © IWA Publishing 2017 W.Item The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru(Springer International Publishing, 2019) Govind, N.R.; Ramesh, H.In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis–Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989–2001 than 2001–2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28 years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6 °C and the mean LST of urban area increased by about 8 °C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future. © 2019, Springer Nature Switzerland AG.Item Multivariate statistics and water quality index (WQI) approach for geochemical assessment of groundwater quality—a case study of Kanavi Halla Sub-Basin, Belagavi, India(Springer editorial@springerplus.com, 2020) B Patil, V.B.; Pinto, S.M.; Govindaraju, T.; Virupaksha, V.S.; Bhat, V.; Lokesh, K.N.Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser–Meyer–Olkin and Bartlett’s test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation. © 2020, Springer Nature B.V.Item Combining agriculture, social and climate indicators to classify vulnerable regions in the Indian semi-arid region(IWA Publishing, 2022) Kalli, R.; Jena, P.R.Climate change vulnerability is highly counter-productive for agriculture among the arid and semi-arid regions. The study constructs the agriculture vulnerability index for Karnataka, a south Indian state. The state has faced frequent climate-related shocks in the last decade. The district-wise vulnerability index is estimated using longitudinal data considering exposure, sensitivity and adaptive capacity as sub-indices. The results show that the districts in the north interior region of Karnataka are highly vulnerable to the climate change followed by the districts in the south interior and coastal regions. There is an urgent need to prioritize the most vulnerable districts while formulating the development policies to minimize the risk of climate change on agriculture. Specific technical knowledge and support need to be made available to the farmers for informative climate resilience action. © 2022 The Authors.Item Future global concurrent droughts and their effects on maize yield(Elsevier B.V., 2023) Muthuvel, D.; Sivakumar, B.; Mahesha, A.Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950–2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950–2014), near future (2021–2060), and far future (2061–2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions. © 2022 Elsevier B.V.Item Assessing forest health using remote sensing-based indicators and fuzzy analytic hierarchy process in Valmiki Tiger Reserve, India(Institute for Ionics, 2023) Roshani; Sajjad, H.; Rahaman, M.H.; Rehman, S.; Masroor, M.; Ahmed, R.Anthropogenic activities, climate variability and environmental stresses have greatly affected forest ecosystems globally. Thus, monitoring of forest health is essential for proper planning and effective management. The present study employed an integrated approach of remote sensing and fuzzy analytic hierarchy process to assess the forest health in the Valmiki Tiger Reserve in India. Advanced vegetation index, normalized difference vegetation index, normalized difference moisture index, forest fragmentation, rainfall and soil types were derived from remote sensing data. Multiple buffer zones of villages, roads, railways and canals were also determined for analyzing the forest health status. These layers were prepared in the geographical information system. These layers were given weightage using fuzzy analytic hierarchy process. These layers were integrated to prepare forest health map using weighted overlay method. The results revealed that the largest forest area was found under moderately healthy forest (37%) followed by healthy forest (31%) and unhealthy forest (13%). Of the total area of the Reserve, 19% area was under non-forest category. Human-induced disturbances such as encroachment, illegal sand mining, livestock grazing and forest conversion to agriculture have been attributed to the unhealthy forest in the Reserve. The receiver operating characteristic curve value and area under curve (0.792) show reliability of forest health map. The findings of this study may be helpful for forest managers, conservationists and local communities in devising sustainable strategies for effective management of the forest. The methodological framework adopted in this study may be utilized in other geographical regions interested in assessing forest health. © 2022, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.Item Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin(Springer Science and Business Media Deutschland GmbH, 2023) Krishnaraj, A.; Honnasiddaiah, R.Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model’s predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India(Springer Science and Business Media Deutschland GmbH, 2024) Naik, N.; Chandrasekaran, K.; Sundaram, V.; Prabhavathy, P.Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Item Deep Learning-Based Hydrological Drought Prediction in the Wardha River Basin, India(John Wiley and Sons Inc, 2025) Janardhana, M.; Kikon, A.Drought is an abnormal condition characterized by dry weather which can continue for days, months, and years. Drought often has major effects on the ecosystems and agriculture of vulnerable regions leading to catastrophe on the local economies. Deep learning was employed in this study to forecast hydrological drought in the Wardha River basin in Maharashtra, Vidarbha region, India. Monthly streamflow data from 1971 to 2020 for the Wardha River serve as the basis for analysis. The study calculates the standardized streamflow index (SSI) at several timescales (3, 6, 9, 12, and 24 months). Deep learning models, specifically the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model, are employed for drought prediction within the study region. The models are trained with data spanning from 1971 to 2005 and tested against data from 2006 to 2020. Predictions are made for lead time scales of 6 and 12 months by considering lagged SSI values. Drought event lead time scale forecasts will serve as an early warning strategy. The 6- and 12-month lead times of the SSI forecast could be used as a warning for anticipated drought conditions. The study assesses model efficiency by comparing the root mean square error (RMSE) and mean absolute error (MAE) between the LSTM and MLP models. The results indicate that the LSTM model performs better for higher time scales in predicting hydrological drought, whereas the MLP model demonstrates superior predictive capabilities for lower time scales of drought index. © 2025 Wiley-VCH GmbH.