In India, the brick industry is one of the fastest-growing industries that support construction requirements. Brick dust is a by-product of the brick industry. Brick dust is utilized for different purposes, such as in the construction of pavement, as a filling material, etc. Brick dust can be used as an admixture to improve soil performance. This study series of Unconfined Compressive Strength (UCS) tests is performed on the soil mixed with brick dust. The impact of cement on the soil and brick dust mix is also investigated. Soil used in this study was obtained from the village of Muzaffarpur. The content of brick dust was varied from 5% to 50%. The cement content was varied from 2% to 6%. Results of UCS have shown that brick dust and cement alone can potentially improve the soil’s strength. Brick dust enhances the soil’s strength by improving its gradation, while cement improves its strength through its cementitious property. The combined effect of brick dust and cement can potentially improve strength more than individual admixtures.

This study examines the chemical properties and micronutrient concentrations of roadside soil in the ecologically sensitive Himalayan foothills of Uttarakhand, India. The primary objective is to assess soil contamination levels and their implications for urban ecosystem services. Soil samples from 13 locations were systematically collected, processed, and analyzed using atomic absorption spectrophotometry. Results indicated average concentrations of Zn, Cu, Fe, Mn, and Ni at 9.94, 3.28, 24.32, 17.35, and 0.28 mg/kg, respectively. Significant variations in soil chemical properties and micronutrient levels (p < 0.05) were observed across different sites, excluding phosphate levels. These findings underscore the critical need for targeted environmental management strategies to mitigate soil contamination and preserve vital ecosystem services in urbanized regions. The data provided can guide administrators in developing policies that promote sustainable development and environmental resilience.

Floods are one of the most extreme events that affect human life in different regions worldwide. In India, North Bihar is one of the regions affected by floods every year during the monsoon season. Every year, floods cause the loss of human and cattle lives. It has also become the reason for enormous economic loss. Damage to infrastructure is a significant cause of such economic loss. Every year, transportation infrastructure gets inundated due to damage to the pavement during severe flood conditions. Many researchers have investigated the impact of floods on pavement structures. They have reported the different factors significantly influencing the pavement during flood conditions. Different forms of pavement failure take place due to this. Pavement deterioration models were developed to predict pavement damage. None of the deterioration models are suitable for all regions. The suitability of deterioration models depends upon conditions in the selected region. In this study, the impact of floods on the pavement in North Bihar is investigated. The different forms of pavement failure were identified and their extent was analyzed. Also, a mathematical model was developed to predict different forms of failure due to floods in North Bihar. It was observed that the height of flood has significant impact on the different parameters considered in this study.

The Burhi Gandak River, a significant tributary of the Ganga River and a vital water source in Bihar, India, is facing critical water quality degradation due to rapid population growth, urbanization, and industrial activities. This study conducts a detailed water quality assessment of the river, focusing on the effects of these anthropogenic factors across different locations, seasons, and industrial zones. Water samples were collected and analyzed for key parameters, including pH, total dissolved solids (TDSs), total hardness (TH), biological oxygen demand (BOD), and dissolved oxygen (DO). The results indicate significant spatial and temporal variations, with water quality deteriorating notably in areas like Samastipur and Khagaria, where industrial and urban activities are more concentrated. For instance, BOD levels increased from 7.5 mg/L to 9.5 mg/l as the river flows through urban Samastipur, signaling a decline in water quality. Additionally, sugar mills located along the river contribute to higher pollution levels during operational months, especially in Lauria and Sugauli. Seasonal analysis shows that the lean season experiences the highest levels of degradation, while monsoon floods increase suspended solids due to sediment inflow. These findings emphasize the urgent need for improved regulation of industrial discharge and urban waste management to protect the water quality of the Burhi Gandak River and the livelihoods dependent on it.

Environmental problems and pollution are the most critical issues worldwide. Ecological awareness is necessary to protect the environment from pollution. According to a research investigation, around 62 million people are in Tirupati in India. The water of this location is highly polluted and needs treatment. This study analyses the removal of heavy metals with the ion exchange process with the addition of membrane filtration. The wastewater has present heavy metals, which are required to remove. The basic idea of this study is to protect the environment and people and provide them with fresh water. This study focuses on the treatment process and survey of generated wastewater from the Swarnamukhi River of Tirupati. The major issue in the water stream is the presence of TSS, TDS, BOD, COD, heavy metals, and fecal coliform bacteria. The evaluation of the treatment process is briefly described and designed in this study as the reclamation of wastewater. The removal efficiencies of total suspended solids (TSS) and total dissolved solids (TDS) were found to be 95% and 93.33%, respectively. The removal efficiencies of BOD and COD were found to be 76.25% and 85%, respectively. The ion exchange process with the addition of membrane filtration is the effective method to remove BOD, COD, and heavy metals from water.

The life cycle installation process for wastewater treatment is the advanced treatment assessment measure for the wastewater treatment and reuse of water resources.Utilizing the methods and concepts of "Life Cycle Assessment" for wastewater treatment and reuse of water is carried out using LCA software like Umberto, SimaPro, Gabi, etc based on the infirmity of calculations of "Life Cycle Inventory Data".LCA method helps in analyzing the various emissions and impacts of the "wastewater treatment plant" (or WWTP).A secondary data analysis study is designed on the basis of various data and information collected from 15 research papers with applicable and defined sources on wastewater treatment plants in India and LCA method.The data analysis configured the positive impact of the researched categories.LCA data analysis provided the idea that positive effects tend to override the negative impacts of recycling wastewater treatment process.The assessed categories for LCA data analysis are "global warming potential", "eco-toxicity potential", "fossil depletion potential" "particulate matter formation" and so on, which are highlighted in this project.The negative impacts can be associated with the effects of untreated sewage and compost produced by the "wastewater treatment process".The project analyses and evaluates the various aspects of the "life cycle assessment" application in the "wastewater treatment plants" in the Indian subcontinent.This is done particularly in the Madurai city, of the Indian subcontinent.The LCA assessment highlights the environmental impact of the water purification processes adopted in the mentioned waterplant.The ecological impact is measured on various scales like "ecological toxicity", "marine toxicity", "freshwater Eutrophication" etc.Similarly, the assessment highlights the impacts on the resources like fossil fuels, and most importantly on the human health.In addition, the project also highlights the various mitigation measures and techniques to increase the ecological efficiency of the wastewater processes.

Rivers are one of the major sources of water, and they fulfil many of the requirements of humans. Rivers provide water for drinking, irrigation, act as a way for transportation, and many other things. Any change in the water of the river quantitatively or qualitatively affects human life severely. In India, the river Ganga is the most essential and holy river. Sedimentation is the most important factor affecting the flow of any river. Natural forces primarily influence it. However, the growing urbanisation of cities along the river’s banks is also influencing the sediment process. Estimation of daily suspended sediment load is a difficult and extensive procedure that includes inaccuracy too. Nowadays, soft computing techniques are frequently used to predict suspended sediment concentrations (SSC). This study outlines the capabilities of a neural network and an adaptive neuro-fuzzy inference system in estimating the suspended sediment load of the Ganga at Varanasi gauging stations 25° 19′ 29.45″ N 83° 2′ 9.17″ E. The daily data of discharge (Q) in m3/s and SSC in mg/l from January 2010 to December 2012 is used for training of artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with the three different sets of combination data. The efficacy of these models is checked based on various statistical parameters. The results show that both ANN and ANFIS models work well in suspended sediment concentration prediction. However, the performance ANN (R2 = 0.939 and RMSE = 166.325) model is slightly better than that of the ANFIS models (R2 = 0.938 and RMSE = 168.542) for a range of studies. Thus, ANN and ANFIS can be successfully applied to predict the concentration of sediment load of the Ganga river for the selected zone. However, a wide range of datasets is required to generalise the present models for better prediction of suspended sediment concentration.

Scoring and sedimentation are two continuous processes which take place in river bed. These two phenomena have significant impact over the overall behavior of river. Scoring and sedimentation process get affected due the construction of structures like Bridges. Piers of bridge alter the natural flow of rivers. Due to this scouring process increases near to the pier. The scouring process gets affected due to the other activities like sand mining or filling. Also any obstruction like some construction can also affect the scouring. In this study laboratory tests were conducted to understand the nature of scouring near to the pier. For this purpose, model test were conducted in a straight channel with a model concrete pier. The impact of the obstruction in flow, mining of sand and filling of the sand is investigated through this investigation. The finding of this study can be utilized in understanding and development of techniques of controlling scouring near the pier

The river is an essential resource of fresh water on the earth, and its management is very challenging. Sedimentation and erosion is a very complex process of the river system. Suspended sediment concentration (SSC) plays a key role in this process. Therefore, water resources planning and management are essential for this. Generally, the sediment concentration estimated by direct measurement, but this process is costly and cannot apply in all rivers. It is essential to develop some technology that can predict the suspended sediment concentration. So, in this study, a feed forward back propagation neural network (FFBPNN) and support vector machine (SVM) were used to predict the suspended sediment concentration. One year of daily data was collected from the river Ganga at Varanasi cross-section. The performance of the model estimated for training and validation stages based on root mean square error (RSME), Coefficient of correlation (R) and Nash–Sutcliffe model efficiency (NSE). The performance of applied model indicated that FFBPNN (RSME = 176.2, R = 0.955 and NSE = 0.912) for validation is more precise for suspended sediment load prediction than SVM (RSME = 222.1, R = 0.930 and NSE = 0.864). This study shows that the soft computing technique is a robust tool for SSC prediction.

Suspended sediment concentration (SSC) is one of the remarkable parameters for sediment transport calculation and their estimation. In another hand, the use of the conventional method comes with lots of limitations as compared to acoustic Doppler current profiler (ADCP). It is frequently used for velocity measurement and concentration of suspended sediment estimation (in dB) with the help of acoustic backscatter intensity. In present studying ADCP of 1200 kHz is used along with water sampler. A study area of river Ganga along Varanasi (length 10 km) is divided into six cross-sections for analysis. Suspended samples have been collected for SSC calculation in terms of mg/l. These samples were categorized into two parts; the first part was utilized for calibrating the backscatter and attributing the intensity to suspended particle concentration using a linear regression method. The second part was used for validation of acoustic intensity. Six years of observation data were used from year 2012 to 2017. As a result of backscatter intensity, it was visible that there is an increase in suspended sediment concentration load year by year along the river cross-section. When the bed profile was created, it was found that the bed profile of study cross-section has drastically changed at Raj Ghat cross-section (CS 6). This type of study is essential where river passing near any city because of the high rate of erosion and deposition may change the bed profile; this change can invite natural disaster. Also, this data can be further utilized for developing significant measures for existing Ghats and other hydraulic structures for present and future sustainability.

In recent years, extensive research has been performed when the foundation is placed on the crest of slope. Besides of so many researches less number of researches has been done for foundation is placed on slope considering Soil-Structure Interaction (SSI) effect. Construction on slopes poses more challenges especially under seismic load due to an earthquake in addition to the forces of sliding slope itself. The failure of slope not only affects any structure but also has damaging consequences on the environment in general. Therefore, transmitting boundaries should be adequate to absorb the seismic energy at the boundaries. In this paper, effect of transmitting boundary on soil-slope-foundation interaction (SSFI) is studied. Two cases have analysed for SSFI when foundation is placed at various position on the crest and slope itself. El-Centro earthquake (1940) with three different PGA viz. 0.25g, 0.5g and 1g is applied as input motion for both the cases. It is observed that the foundation placed near the slope is more susceptible to damage. Responses of slope (acceleration and displacement) have also been observed at three different nodal points on slope. Results show that the amplification in soil mass leads to settlement of foundation. Shear stress and equivalent plastic strain distribution for SSFI is discussed for different load conditions. It is found that the slope-foundation system shows the local and global failure. Further Post earthquake peak settlement for foundation is plotted and it shows the true behavior of soil.

Autoregressive integrated moving average (ARIMA) is a data mining technique that is generally used for time series analysis and future forecasting. Climate change forecasting is essential for preventing the world from unexpected natural hazards like floods, frost, forest fires and droughts. It is a challenging task to forecast weather data accurately. In this paper, the ARIMA based weather forecasting tool has been developed by implementing the ARIMA algorithm in R. Sixty-five years of daily meteorological data (1951-2015) was procured from the Indian Meteorological Department. The data were then divided into three datasets- (i)1951 to 1975 was used as the training set for analysis and forecasting, (ii)1975 to 1995 was used as monitoring set and (iii)1995 to 2015 data was used as validating set. As the ARIMA model works only on stationary data, therefore the data should be trend and seasonality free. Hence as the first step of R analysis, the acquired data sets were checked for trend and seasonality. For removing the identified trend and seasonality, the data sets were transformed and the removal of irregularities was done using the Simple Moving Average (SMA) filter and Exponential Moving Average (EMA) filter. ARIMA is based on method ARIMA (p,d,q) where p is a value of partial autocorrelation, d is lagged difference between current and previous values and q is a value from autocorrelation. In the present study, we worked on ARIMA (2,0,2) for rainfall data and ARIMA (2,1,3) for temperature data. As a result, it estimated the future values for the next fifteen years. The root means square error values were 0.0948 and 0.085 for rainfall data and temperature data respectively which show that the algorithm worked accurately. The resulted data can be further utilized for the management of solar cell station, agriculture, natural resources and tourism.

Background:Stiffened panels are being used as a lightweight structure in aerospace, marine engineering and retrofitting of building and bridge structure. In this paper, two efficient analytical computational tools, namely, Finite Element Analysis (FEA) and Artificial Neural Network (ANN) are used to analyze and compare the results of the laminated composite 750-hat-stiffened panels.Objective:Finite Element (FE) is an efficient and versatile method for the analysis of a complex problem. FE models have been used to generate data set of four different parameters. The four parameters are extensional stiffness ratio of skin in the longitudinal direction to the transverse direction, orthotropy ratio of the panel, the ratio of twisting stiffness to transverse flexural stiffness and smeared extensional stiffness ratio of stiffeners to that of the plate.Results and Conclusion:For training of ANN, multilayer feedforward back-propagation has been used as a network function with two-hidden layers in the neural network. The good network architecture is achieved after several iterations to predict the buckling load of the stiffened panel. ANN prediction for unknown new data set is in good agreement with FEA results of different cases, which show that ANN tool can be used for the design of complex structural problems in civil engineering and optimization of the laminated composite stiffened panel.

Proper management of water resources for forth coming generation is of prime concern due to pressure of increasing population and water requirement by crop. Exact scheduling of crop requires the accurate quantification of water requirement for crop which is way towards management of water. Determination of irrigation scheduling and water requirements for crops required to calculate reference evapotranspiration. Estimation of evapotranspiration which plays an important role in the water budgeting and perhaps the most challenging component of hydrological cycle to estimate especially in semi-arid and arid regions. Evapotranspiration estimation methods have their own strengths and restrictions depending upon the availability of data and methods applied with assumptions used. Present paper reviews the most commonly used various approaches, which are suitable for the estimation of evapotranspiration.

Sand mining is proposed at Alappad, Panmana, and Ayanivelikulangara of Kollam District within an area of 180 ha because of which nearly 550 families are being exposed to the impact of this mining. Families suffer from various problems associated with the mining activity, which includes environmental, social, and economical health. In addition, they have to be rehabilitated to other acceptable areas. The Resettlement & Rehabilitation (R&R) plans are an integral part of this EIA (Environmental Impact Assessment) study. Hence, this issue needs to be carefully studied and solved in an amicable manner. The objective of the present study is to ascertain the socioeconomic and other impacts on the people and on the area of operation and preparation of R&R plan for the project-affected families in the 180 ha mine lease area in line with Indian Rare Earth’s (IRE’s) R&R plans. We need to identify reasons of various social–political driving forces causing complaints and obstruction of existing in proposed mining and work out mechanisms for consultation with all stakeholders and influential forces in order to address issues related to mining.

Soil and water come under the category of the most important renewable natural resources on earth. But due to their indiscriminate use, they can be seriously endangered. Hence it is essential to manage natural resources sustainably to preserve them for the future. For soil prevention, Morphometric analyses only drainage pattern. However, Land Use/Land Cover (LULC) and soil characteristics should also be considered. Thus, it is necessary to combine all methods of prioritization to provide reliable results. As the Ganga watershed in India is currently under environmental distress, therefore in the present study, Morphometric, land use/land cover and Universal Soil Loss Equation (USLE) analysis are done in this watershed using ArcGIS and ArcSWAT. To identify the erosion prone areas for effective planning and management of groundwater resources the study area was divided into seventeen sub-watersheds for the analysis. The mainstream of the basin is of eighth order, and drainage patterns of sub-basins are mostly of sub dendritic to dendritic. Various linear, areal and relief parameters of each sub-watersheds have been determined, and sub-watersheds are prioritized by ranking them according to erodibility characteristic. The results reveal that according to Morphometric analysis out of seventeen sub-watersheds, Rajpura Region, Narahi Region and Mughal Sarai Region Sub-watersheds are subjected to more soil erosion, and Sarai Meer Region, Balia Region and Ghazipur Region Sub-watersheds are subjected to least soil erosion. Whereas according to LULC and USLE analysis Narahi Region, Pipra Region, Gahmar Region Sub-watersheds and Rajpura Region and Mughal Sarai Region Sub-watersheds are likely to be subjected to more soil erosion respectively. Sarai Meer Region, Lalganj Region, Pindra Region Sub-watersheds and Sarai Meer Region, Lalganj Region, Magharia Region Sub-watersheds are subjected to least soil erosion according to LULC and USLE analysis respectively. Integrating all the three methods, we found that Chandauli Region, Narahi Region and Mirzapur Region Sub-watersheds are suffering from severe soil erosion problem and Sarai Meer Region, Lalganj Region and Ghazipur Region Sub-watersheds have least soil erosion. These results can be further used for soil erosion and sediment yield modeling projects.

Water is the remarkable natural resource which is essential for every form of life on the planet “Earth,” and nowadays India is facing major water scarcity problems. These problems are due to climate change and urbanization. As the urbanization is increasing, the percentage of impervious land is increasing which is leading to increase in urban runoff and lesser recharge of underlying aquifers, leading to water scarcity at local scale as runoff is not tapped and utilized. Watershed modeling is considered as one of the most important aspects of planning and development for natural resources for water conservation measures. Watershed modeling is useful for completing and implementing plans, programs, and projects to sustain and increase watershed utilities that directly affect the biotic and abiotic communities within watershed boundary. In this paper, we have taken Rajiv Gandhi South Campus (RGSC), Barkachha, which is the extension of Banaras Hindu University and facing serious water scarcity problems. Here, firstly runoff was calculated by SCS-CN method using 10 years of rainfall data, and then flow accumulation and sink map were created using DEM. Watershed investigation has been done to suggest some hydraulic structures using flow accumulation, sink map, and runoff data, for the study area, mainly to enhance the availability of water for agricultural activity and university development.

— The study evaluates forecasting of groundwater level for short period of data by utilizing the standard artificial neural network (ANN) model, trained with two back propagation (BP) training algorithms namely Levenberg-Marquardt (LM) and Gradient Descent with Momentum (GDM). Data of five wells, Annual rainfall, Temperature, Relative humidity and river stage are chosen as input parameters.The model efficiency and accuracy were measured based on the root mean square error (RMSE) and regression coefficient (R).R-values approach towards the unity for most of the wells in LM method. LM method is recommended for forecasting ground water level for short duration of data and also it is anticipated that this method will give fairly accurate result for long duration of data under consideration. In case of constraint on data availability mentioned above, the LM Method is found to be suitable for ground water forecasting even when we take river water level as one of the inputs in ANN model.