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Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km

The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM). SWAT-CRM along with other thematic layers was combined using weightage overlay analysis to prepare the maps of groundwater vulnerability index (VI). The weight scale (w) and rating scale (r) were assigned based on a survey of available literature, and we then amended them using the analytical hierarchy process (AHP) and a probability frequency ratio (PFR) technique. Results depicted that the region under high groundwater vulnerability was found to be 5-22% using traditional recharge maps, while those are 9-23% using improved SWAT-CRM. The area under the curve (AUC) revealed that groundwater vulnerability zones predicted with SWAT-CRM outperformed the DRASTIC model applied with the traditional recharge map. Groundwater electrical conductivity (EC) was2500 mS/cm in the high groundwater vulnerability zones, while it was1000 mS/cm in the low groundwater vulnerability zones. The outcomes of this study can be used to improve the sustainability of the groundwater resources in IIB through proper land-use management practices.

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were $${X}_{t-1},\; {X}_{t-2}, \;{X}_{t-3}, \;{X}_{t-4}, \; {X}_{t-12}$$ X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE $$\approx$$ ≈ 0.08 m), mean absolute error (MAE $$\approx$$ ≈ 0.06 m), and coefficient of determination (R2$$\approx$$ ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

In arid and semi-arid regions, groundwater is considered being the most available natural resources for different water use. However, it is being limited in quantity. As such, its sustainable development and managementent depends on based on various criteria (e.g. climatic conditions, scale, aquifer properties, etc.). This study presents three multi-criteria index approaches (Analytic Hierarchy Process (AHP), Best–Worst Method (BWM), and Full Consistency Method (FUCOM) to classify groundwater potential maps in the Sarakhs Plain in North-east Iran. In this study, 10 parameters (layers) that affect groundwater potential recharge mapping (GPRM) are used using ArcGIS10.2. These layers includeground surface elevation, surface slope, aspect, relative slope position (RSP), plan curvature, topographic wetness index (TWI), terrain ruggedness index (TRI), drainage density, landuse, and lithology. These layers and their features were assigned properweights based on the conceptual frameworks of AHP, BWM, and FUCOM techniques, and then using a weighted overlay summation process (WOSP), final maps of groundwater potential in Sarakhs plain are obtained. The developed groundwater potential maps are classified into four classes, including low, medium, high, and very-high. The results show that among the 10 driving parameters, land use, and lithology have the highest importance and the surface slope has the lowest importance in the mapping of groundwater potential recharge. The best groundwater potential zones are concentrated in northeast and southeast, central parts, and a few parts in the areas of the western regionof the Sarakhs plain due to its nearly gentle slopes with quaternary alluvial and agriculture land and lower drainage density. The obtained results are of high value for decision-makers in the Sarakhs plain in specific and for entire Iran in general to apply sustainable groundwater utilization plans.

Soil erosion causes significant damage to humans by reducing soil productivity and filling reservoirs from sediment deposition in Narmada Basin, India; hence, it is important to recognize soil erosion prone areas for preventive steps in this basin. In this research, prioritization of sub-watersheds of Narmada Basin has been done using game theory-based approaches such as Condorcet and Fallback bargaining. For this purpose, Digital Elevation Model (DEM) generated by Shuttle Radar Topography Mission (SRTM) was used to extract and analyze 12 morphometric parameters including linear, aerial, and relief parameters. Based on the Condorcet and Fallback bargaining methods, the Mohgaon watershed came at the first priority ranking, that means it’s the most vulnerable watershed from the point of soil erosion (SE). Game theory was successfully implemented for prioritizing watersheds in term of SE. The findings showed that morphometric parameters and game theory approach have a high efficiency in recognizing areas that are vulnerable to erosion.

The application of Artificial Intelligence (AI) techniques has become popular in science and engineering applications since the middle of the twentieth century. In this present study, three AI techniques (ANFIS, GP and ANN) have been used for forecasting streamflow into Shakkar watershed (Narmada Basin), India. The models have been used considering previous streamflow and cyclic terms in the input vector to provide a suitable time series model for streamflow forecasting. To evaluate the model performance, RMSE, MAE, CORR and CE were employed. Results showed that the ANFIS has the best performance in forecasting streamflow time series for Shakkar watershed. The GP and ANN are in the 2nd and 3rd ranks, respectively. According to the results, in all the AI methods (ANFIS, GP and ANN), the model with cyclic terms had better performance compared to those models not considering periodic nature and being applied by only considering the previous streamflow.

Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods.

Runoff–sediment process modeling is highly variable and nonlinear in nature. For sediment yield prediction, the difficulty of rainfall–runoff–sediment yield hydrological processes remains challenging. The present study uses a simple nonlinear dynamic (NLD) model to predict daily sediment yields, taking into account the degree of daily–sediment yield in catchment areas, and its findings were compared to three widely used models including artificial neural networks (ANN), support vector machine (SVM), and gene expression programming (GEP). The daily measured discharge–sediment data for 25 years were obtained from Shakkar Watershed; Central India as in the current study. The coefficient of correlation (CC), Nash-Sutcliff (NS), and root-mean-square error (RMSE) were employed to assess the performance of the models. The results show that the NLD model was found better than ANN, SVM, and GEP model. These models had correlation coefficient (CC = 0.975, 0.887, 0.843, and 0.901), root-mean-square error (RMSE = 0.748, 1.751, 1.961, and 1.545), and Nash–Sutcliffe efficiency (0.952, 0.784, 0.673, and 0.814) correspondingly. Hence, the NLD model can be used for predicting sediment. In order to implement appropriate measures of soil conservation in the watershed to reduce the sediment load in the river, predicting the sediment yield is very necessary to maximize the life of the structure.

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.

In the current work, we investigated the concentration of Ni and Pb in different organs of Phragmites australis to evaluate its potential application as a phytoremediator to remove these two metals from contaminated water and sediment in Lake Burullus (a Ramsar site in Egypt). Above- and below-ground biomass of P. australis, water and sediment were sampled monthly for 1 year at six sites of Lake Burullus (three sites represent each of the northern and southern parts of the lake) using six randomly distributed quadrats (each of 0.5 × 0.5 m) at each sampling site. Significant variation was detected for Ni and Pb concentrations in the sediments and waters between the northern and southern sites of the lake. The biomass of P. australis in the southern sites was greater than that in the northern sites; in addition, the above-ground biomass was higher than the below-ground biomass. The above-ground organs accumulated higher concentrations of Ni and Pb than the below-ground organs. The Ni and Pb standing stocks data indicated that the organs of P. australis extracted higher amounts of Ni and Pb per its area from the southern rather than the northern sites. In the current study, the Ni and Pb above-ground standing stocks increased from the early growing season (February) and reached its peak during August and then decreased. The highest monthly Ni and Pb standing stock (18.2 and 18.4 g m− 2, respectively) was recorded in the above-ground organs of plants in the southern sites in August. The bioaccumulation factor of Ni was 157.6 and 153.4 in the northern and southern sites, respectively, whereas that of Pb was 175.3 and 158.3. The translocation factor of Ni and Pb from the below- to above-ground organs was generally > 1. Thus, this reed species is a potential candidate for Ni and Pb phytoextraction. Based on our results, P. australis could be used for the extraction of Ni and Pb to reduce the pollution in Lake Burullus, if the above-ground biomass is harvested at its maximum value in August, as was the case regarding the maximum standing stock of Ni and Pb.

The sources of land degradation in many watersheds are runoff and erosion. In order to delay this degradation resulting in the loss of soil fertility, a system for its quantification needs to be established. Erosion severely impacts our natural capital and requires several studies and prevention and management steps. This research focuses on validating the revised universal soil loss equation (RUSLE) method through evidence from remote sensing (RS) and the Geographic Information System (GIS) in the watershed of the Inaouene river upstream of the Idriss 1st dam in the Taza region (northeast of Morocco). In this context, the approach adopted was performed, consists in combining the revised universal soil loss equation (RUSLE) to quantify erosion and GIS to spatialize the factors responsible for water erosion and soil loss (climate, soil, vegetation, and topography) GIS to produce a map of erosion risk. The results achieved show that the Inaouene watershed is characterized by an average climatic aggressiveness, a sparse vegetation cover without any safeguards, moderate to high erodible soils with a maximum ranging from 50 to 150 t/h/year. Topography characterized by high (50%) to moderately broken reliefs. The human intervention remains the most mattering factor in the embrittlement and the accentuation of the soils vulnerability for erosion for every dominant natural factor. The results of this study could be of use in prioritizing areas for soil conservation measures and watershed development and management. It is a tool for decision-makers, planners, and decision-makers in soil and water conservation decision-making to implement anti-erosion strategies to reduce the impact of erosion.