Kriging Interpolation Technique Research Articles

Mapping and Quantifying Integrated Land Degradation Status of Goa Using Geostatistical Approach and Remote Sensing Data

Globally, land degradation is becoming a grave concern. Over the years, conditions such as drought, extreme weather events, pollution, changes in land use land cover, and desertification have intensified and led to land degradation, affecting both ecological and economic processes. Equally, during the last two centuries, population and urbanization have amplified manifold and increased the demand for additional food and shelter, resulting in alteration in land use land cover, over-grazing, and over-cultivation, loss of nutrient-rich surface soil, greater runoff from the more impermeable subsoil, and reduced water availability. Geographically, Goa is a highly diversified state. It is sandwiched between the West Coast and the Western Ghats. The state is blessed with beaches, mangroves, backwaters, wetlands, wildlife sanctuaries, evergreen forests, barren lands, and other vital ecosystems. The State of Goa, on average, receives more than 3000 millimeters of rainfall annually with high surface runoff. Using both primary and secondary data, this study sought to investigate and quantify the state’s land degradation. Secondary data came from satellites and other sources, while primary data came from field observation and ground truthing. Land degradation factors related to soil loss and the spatial pattern of soil erosion are predicted and evaluated using the Revised Universal Soil Loss Equation (RUSLE) method. Landsat-8 OLI-TIRS images were utilized to decide land use and cover (C factor), while DEM information was utilized to assess (LS factor). A soil map and rainfall data were collected to acquire a better understanding of soil erodibility (K factor) and rainfall erosivity (R factor). The kriging interpolation technique was used to gain a deeper comprehension of land degradation.The purpose of this paper is to comprehend the concept of integrated land degradation and how it affects the environment of Goa. Using remote sensing data and geostatistical methods, the study creates a comprehensive map of land degradation in the region by identifying and analyzing the various forms of land degradation in Goa. The paper also looks at how rainfall and the amount of land cover affect the rate of soil erosion in Goa. According to the findings, intense rainfall makes the eastern part of Goa particularly susceptible to soil erosion, and bare soil has a greater potential for erosion than vegetated land. The paper concludes that comprehensive land degradation mapping can be a useful tool for developing efficient land management strategies to preserve soil and encourage sustainable development in the region.

Geomagnetic storm effect on equatorial ionosphere over Sri Lanka through total electron content observations from continuously operating reference stations network during Mar–Apr 2022

Abstract The technological advancements in the current era have highlighted the increasing significance of satellite-based positioning, navigation, and timing services in a wide range of dynamic and critical applications. This has led to significant efforts towards enhancing the performance of global navigation satellite systems (GNSS) operating under challenging ionospheric conditions. The Sri Lankan ionosphere region is a focal point of equatorial aeronomy scientists, being situated in the southernmost landmass of the Indian longitude sector within the vicinity of the magnetic equator where a combination of electric, wind, and temperature dynamics exerts a substantial influence on the ionosphere but was relatively unexplored in the past. In the present work, we employed a Kriging interpolation technique on the total electron content (TEC) variables from ten GNSS receivers operating under the Continuously Operating Reference Stations (CORS) network in Sri Lanka first ever of its kind to deliver two-dimensional regional ionospheric TEC maps at hourly intervals, both during quiet and disturbed ionospheric conditions in the equinoctial March and April months of 2022. The latitudinal variation patterns are discernable from the hourly TEC maps. Furthermore, a comparative analysis of the performance of GNSS-derived TEC with that of the routinely published Global Ionospheric Maps (GIMs) confirms overestimation characteristics of the latter irrespective of the local time of observation. The generated regional ionospheric maps are fairly responsive to the onset of the storm and the recovery phase thereafter. The extent of nighttime ionospheric irregularity is also probed through the rate of TEC index (ROTI) variations, demonstrating that the irregularities were insignificant during the selected storm event.

Assessment and Mapping of Soil Fertility Status of Migna Kura Kebele, Wayu Tuka District, East Wollega, Oromia, Ethiopia

Soil fertility decline is a significant obstacle to Ethiopia's increased food production, but information on current soil fertility status among the study areas is inadequate. Accurate soil fertility data is crucial for implementing effective strategies and developing targeted interventions to improve agricultural productivity and food security in Ethiopia. This study was initiated in this context with the aim of mapping the spatial distribution of specific soil parameters and assessing the status of soil fertility in Migna Kura Kebele, Wayu Tuka District, East Wollega, Ethiopia, providing crucial information for agrarian development. A total of 32 samples of surface soil were drawn at Stratifying random for laboratory analysis in 2019 G.C. Maps of soil fertility status were created using ArcGIS 10.4.1 and the common Kriging interpolation technique. Clay loam and clay are the two types of soil in the research study area. The bulk density of the soil ranged from 1.13 to 1.46 g/cm3, while the total porosity ranged from 42.57 to 55.15%. The pH of the soil can range from 6.7, which is slightly acidic, to 4.91, which is strongly acidic. The range of soil exchangeable acidity levels was 0 to 2.46 cmol (+) kg-1, while the range of OC values was medium (1.79% to 3.51%). Soil total N and available P values were between very low and low (0.19 to 1.11%) and 6.71 to 13.44 mg kg-1, respectively. Exchangeable Ca, Mg, K, and Na levels in the soil ranged from 6.93 to 51.15, 2.85 to 23.63, 0.21 to 1.76, and 0.05 to 0.71 cmol (+) kg-1, respectively, while a medium to very high degree of CEC (22.19 to 77.42 cmol (+) kg-1) was observed. A moderate to very high rating was assigned to soil PBS, which ranged from 42.13 to 98.30%. Micro nutrients; Fe, Mn, Cu, and Zn extractable from soil by DTPA ranged in value from 12.07 to 33.51 mg kg-1, 16.02 to 48.26 mg kg-1, 0.18 to 0.62 mg kg-1, and 0.19 to 0.62 mg kg-1, respectively. The soil fertility map was created for the following parameters: pH, OC, total N, available (P, S), CEC, PBS, Ca, Mg, Fe, and Mn; Cu, Zn, and B. According to the study's findings, the study area's soils were found to be deficient in six nutrients that limit yields: N, P, K, S, B, and Zn. he soil in the study area is affected by soil acidity; for sustainable crop production in the study area, soils should be amended with lime, organic matter, and fertilizers of N, P, S, K, B, and Zn.

Peat depth mapping using grid interpolation from terrestrial peat drilling points data: A case study in Zamrud National Park

Zamrud National Park is the peak of the peat dome in the Kampar Peninsula. Peat depth data is needed as a baseline to maintain the balance of the water reservoir and carbon stock. Currently, available peat depth data is predictive photogrammetric data. Soil survey activities need to be carried out to provide accurate and up-to-date data. This research aims to map the peat depth using the best grid interpolation technique. Peat depth measurements were carried out terrestrially, while peat depth mapping used inverse distance weighted, kriging, and spline interpolation techniques. Drilling observation points are arranged based on a system of longitudinal transects and transverse transects in a northeast-southwest direction. The distance between longitudinal transects is 500 m and between transects is 1,000 m. The results showed that the Zamrud National Park area has diverse peat depths ranging from 5.3 to 16.7 m. The Kriging interpolation technique gives the best mapping accuracy results. The standard deviation value is 1.9 is smaller, the accuracy value is 98.82% higher, and the thickness of the dominating peat is 8.4-9.4 m for 7,093.47 ha (24.42% of the total park area).

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009–2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Groundwater Availability and Suitability Evaluation for Irrigated Agriculture in Mbale District

The purpose of this study was to evaluate the availability and suitability of groundwater in Mbale district basing on potential, quality and vulnerability to support irrigated agriculture. Groundwater potential was assessed using geospatial technique through AHP model. Thematic layers were prepared namely: land use and land cover, slope, soil, rainfall, lineament density, lithology, geomorphology and drainage density. All layers were integrated using the MLC technique. Ranking of each parameter was performed using WOIA. Weights were assigned to each subject class basing on AHP results. The consistency of the outputs was tested by computation of CR and was at a reasonable acceptable level (0.029 < 0.1). Groundwater potential was delineated basing on the values of groundwater potential index. Groundwater quality was determined using IWQI method. Thirty-three water samples were collected. Five chemical parameters were tested in the laboratory: chloride, electrical conductivity, bicarbonate, sodium and SAR to generate the water quality database using Kriging interpolation technique. Computation of IWQI values for each source was made and was used to generate the IWQI map using the weighted summation. DRATIC method was used to delineate vulnerability of groundwater. Layers were generated using the following parameters: depth to groundwater, net recharge, aquifer media, topography, vadose zone impacts, and hydraulic conductivity. The aquifer vulnerability map was prepared by overlaying layers. Three different vulnerability zones were determined according to DRASTIC scores low (<100), medium (100-140) and high (>140). Integration of maps of quality, vulnerability and potential of groundwater was made using an unsupervised MLC classification method. Groundwater potential was in three zones: very good zone was 26.99 km2 (12.98%), the good zone was 126.22 km2 (60.71%) and the poor zone was 54.69 km2 (26.31%). The average annual exploitable groundwater reserves are estimated at 0.026 (MCM/km2) in the zone of “very good”, 0.024 MCM/km2 in the zone of “good” and 0.018 MCM/km2 in the zone of “poor”. Groundwater quality was in two use restrictions: High Restriction of 90.90% with the IWQI value from 40 to 55 on the area of 188.98 km2 of the study area and Severe Restriction of 9.10% with IWQI values from 32 to 40 covering of 18.92 km2. The study area had a high probability of contamination. Low vulnerability covers an area of 11.23km2 (5.40%), 77.83km2 (37.44%) for medium and 118.83km2 (57.16%) for high. The resulting clustered map was classified into five categories with their respective regions: 17.58% very poor (36.56km2), 13.84% poor (28.77km2), 12.69% good (26.39km2), 31.46% very good (65.39km2) and 24.43 % excellent (50.78km2). The sub counties in Mbale district that have inadequate and unsuitable groundwater for irrigated agriculture are Budwale, Wanale, Busano, Bubyangu, Bufumbo and Nyondo since they belong to the zones of very poor and poor. The remaining sub counties have available and suitable groundwater for irrigated agriculture since they belong to the zones of good, very good and excellent.

Soil pH mapping as a function of land use, elevation, and rainfall in the lake tana basin, northwestern of ethiopia

AbstractSoil acidity has become a serious problem in the northern highlands of Ethiopia, limiting land productivity mainly that of crop yield. Research had been done to try to come up with results showing the area was affected by severe acidity, but based on few watersheds and districts in a haphazard way. The objective of this research was therefore to develop a comprehensive soil pH map that covers a broader area of 27 districts covering 2,810,055 ha of land to assess the degree of soil acidity in the Tana Sub‐basin, northwestern highlands of Ethiopia. Using GPS (3‐m precision), 2652 soil point data were collected. Precipitation, elevation, and land use cover were taken into account when creating an interpolated soil pH map. Using adjacent probed pH values, ordinary kriging interpolation (R2 = 0.65; root‐mean‐square error = 0.37125) technique was used to estimate the unmeasured locations, while the spatial pH variability was mapped using the geostatistical (GS+10) software. All Food and Agriculture Organization of the United Nations soil pH categories, with 11.22%, 19.97%, 18.77%, 14.59%, 9.64%, and 6.29% of the soil samples being extremely acidic, strongly acidic, moderately acidic, slightly acidic, neutral, moderately alkaline and strongly alkaline, respectively, were observed. The interpolation result showed a moderate coefficient of variation (10.52%) indicating that the research area had medium variability. In addition, the nugget‐to‐total semivariance ratio ranged from 25% to 75%, indicating moderate geographic dependence. The pH semivariograms best fitted the exponential model, which means the one with maximum (R2 = 0.834) and with minimum residual sum of squares (1.819E‐06) value for mapping of pH. Our result showed that soil pH was inversely dependent on precipitation and altitude, while it varies with land use type. The research result/map could give useful tools for effective integrated land management to minimize soil acidification by policymakers, agriculturalists, and other stakeholder groups. We also recommended future routine updates on the size and distribution of surface soil acidity in the study area.

Characterizing large-scale weak interlayer shear zones using conditional random field theory

The shear behavior of large-scale weak intercalation shear zones (WISZs) often governs the stability of foundations, rock slopes, and underground structures. However, due to their wide distribution, undulating morphology, complex fabrics, and varying degrees of contact states, characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging. This study proposes an analytical method to address this issue, based on geological fieldwork and relevant experimental results. The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters. The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC, enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station. The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5–1 times the main powerhouse span, showing local activation. Furthermore, the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0–0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

Identification of the Groundwater Quality and Potential Noncarcinogenic Health Risk Assessment of Nitrate in the Groundwater of El Milia Plain, Kebir Rhumel Basin, Algeria

In this study, we analyzed the quality and the potential noncarcinogenic health risk of nitrate in groundwater in the El Milia plain, Kebir Rhumel Basin, Algeria. Moran’s I and the ordinary kriging (OK) interpolation technique were used to examine the spatial distribution pattern of the hydrochemical parameters in the groundwater. It was found that the hydrochemical parameters Ca, Cl, and HCO3 showed strong spatial autocorrelation in the El Milia plain, indicating a spatial dependence and clustering of these parameters in the groundwater. The groundwater quality was evaluated using the entropy water quality index (EWQI). The results showed that approximately 86% of the total groundwater samples in the study area fall within the moderate groundwater quality category. The spatial map of the EWQI values indicated an increasing trend from the south-west to the northeast, following the direction of groundwater flow. The highest EWQI values were observed near El Milia city in the center of the plain. This spatial pattern suggests variations in groundwater quality across the study area, with potentially higher risks near the city center. The potential noncarcinogenic health risks associated with nitrate contamination in groundwater for adults and children through the drinking water pathway were assessed using the hazard quotient (HQ). The results revealed that approximately 5.7% of the total groundwater samples exceeded the HQ limit for adults, indicating potential health risks. Moreover, a higher percentage, 14.28%, of the total groundwater samples exceeded the HQ limit for children, highlighting their increased vulnerability to noncarcinogenic health hazards associated with nitrate contamination in the study area. Taking timely action and ensuring strict compliance with regulations in groundwater management are crucial for protecting public health, preserving the environment, addressing water scarcity, and achieving sustainable development goals.

Mapping the disparities in intimate partner violence prevalence and determinants across Sub-Saharan Africa.

This study aimed to map disparities in prevalence and associated factors across countries in Sub-Saharan Africa. We used National Demographic and Health Survey (DHS) data from 26 countries in the region with 114,340 participants. Women and girls in the reproductive age group of 15 to 49 years were included in the study. To map disparities across countries and their provinces, we employed the kriging interpolation technique. We used STATA for data management. The prevalence of physical, emotional and sexual IPV in Sub-Saharan Africa was 30.58, 30.22, and 12.6% respectively, and at least one form of IPV was 42.62%. Disparities were observed across the countries and provinces in each country. Younger age, secondary-level education and above, moderate participation in decision-making, not working out of home, not afraid of the spouse, rich (wealth index), not having a child, high maternal literacy, and rural residence relatively decreased the odds of IPV. The husbands' lower education, alcohol consumption, and high controlling behavior increased the probability of IPV. The prevalence of Intimate Partner Violence in Sub-Saharan Africa is the highest in the world, a signal that the global agenda to end all forms of violence against women will be difficult to achieve. There is a large gap across countries and provinces in each country. Area-specific intervention packages that focus on modifiable factors should be strengthened.

Analysis, spatial distribution and ecological risk assessment of heavy metals in surface soils, the case of Khorramabad, Iran

ABSTRACT In this study, the contents of potentially toxic elements (Cd, Cr, Pb, Cu, Zn, Ni and Mn) in urban surface soils collected from city of Khorramabad were determined to assess the soil contamination rates, potential ecological risks and also spatial distribution of the analyzed elements. In so doing, a total of 55 surface soil samples were collected from 11 sampling sites. After acid digestion of samples, the contents of the elements were determined using flame atomic absorption spectrometer. Then, the pollution rates and potential ecological risks of the soil samples were assessed using various models including geo-accumulation index (I-geo), potential ecological risk index (RI), enrichment factor (EF) and pollution load index (PLI). The spatial distribution of the analyzed elements was estimated using the kriging interpolation technique. Based on the results, the observed mean I-geo values could be classified as “unpolluted” for all heavy metals in the study area. Besides, the EF values of Fe (1), Zn (340), Cr (445), Cd (1000), Ni (605), Cu (874), Pb (316) and Mn (900) indicated that all the elements had extremely severe enrichment. Moreover, the RI values indicated that the whole study area could be categorized as 'moderate'. The values of PLI indicated that the study area could be considered as 'non-polluted'. The calculated values of the studied indices and their spatial distribution showed that the elements concentration in E (G3T4S1) and I (G6T2S2) sampling sites were more than those in the other sites and that, for managing and controlling the anthropogenic activities in those sites, special care and appropriate interventions are needed. In conclusion, while an increase in soil contamination in the study area is expected in the future, the use of bioaccumulator species for biomonitoring and bioremediation of the contaminated areas with potentially toxic elements (PTEs) is recommended.

Optimal redesign of coastal groundwater quality monitoring networks under uncertainty: application of the theory of belief functions.

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion.

Generation and Evaluation of CPT Data Using Kriging Interpolation Technique

The cone penetration test (CPT) has been the de facto field exploration method in geotechnical engineering for decades. Variations of CPT can measure parameters for seismic, environmental, and hydrological applications. Analyzing response often requires properties in areas that have little or no data. Therefore, given the limited CPT data, it is critical to understand how to accurately estimate the soil properties at unsampled locations. In this paper, we generated soil shear wave velocity profiles using the kriging interpolation technique and assessed their performance using site response analysis. Four kriging interpolation-based shear wave velocity profiles and four additional CPT-based shear wave velocity profiles defined site conditions for response analysis. We performed a series of 1-D equivalent linear site response analyses using STRATA software. The site response analysis results are presented as amplification factors (AF), peak ground acceleration (PGA) profiles, surface spectral acceleration, and surface acceleration time histories. Compared to CPT-based profiles, the results of kriging interpolation-based profiles were evaluated and discussed. The results confirmed the reliability of the kriging interpolation technique in predicting soil parameters at unsampled locations.

Growth of water hyacinth biomass and its impact on the floristic composition of aquatic plants in a wetland ecosystem of the Brahmaputra floodplain of Assam, India.

Inland water plants, particularly those that thrive in shallow environments, are vital to the health of aquatic ecosystems. Water hyacinth is a typical example of inland species, an invasive aquatic plant that can drastically alter the natural plant community's floral diversity. The present study aims to assess the impact of water hyacinth biomass on the floristic characteristics of aquatic plants in the Merbil wetland of the Brahmaputra floodplain, NE, India. Using a systematic sampling technique, data were collected from the field at regular intervals for one year (2021) to estimate monthly water hyacinth biomass. The total estimate of the wetland's biomass was made using the Kriging interpolation technique. The Shannon-Wiener diversity index (H'), Simpson's diversity index (D), dominance and evenness or equitability index (E), density, and frequency were used to estimate the floristic characteristics of aquatic plants in the wetland. The result shows that the highest biomass was recorded in September (408.1 tons/ha), while the lowest was recorded in March (38 tons/ha). The floristic composition of aquatic plants was significantly influenced by water hyacinth biomass. A total of forty-one plant species from 23 different families were found in this tiny freshwater marsh during the floristic survey. Out of the total, 25 species were emergent, 11 were floating leaves, and the remaining five were free-floating habitats. Eichhornia crassipes was the wetland's most dominant plant. A negative correlation was observed between water hyacinth biomass and the Shannon (H) index, Simpson diversity index, and evenness. We observed that water hyacinths had changed the plant community structure of freshwater habitats in the study area. Water hyacinth's rapid expansion blocked out sunlight, reducing the ecosystem's productivity and ultimately leading to species loss. The study will help devise plans for the sustainable management of natural resources and provide helpful guidance for maintaining the short- to the medium-term ecological balance in similar wetlands.

Learning ensembles of deep neural networks for extreme rainfall event detection

Accurate rainfall estimation is crucial to adequately assess the risk associated with extreme events capable of triggering floods and landslides. Data gathered from Rain Gauges (RGs), sensors devoted to measuring the intensity of the rain at individual points, are commonly used to feed interpolation methods (e.g., the Kriging geostatistical approach) and estimate the precipitation field over an area of interest. However, the information provided by RGs could be insufficient to model complex phenomena, and computationally expensive interpolation methods could not be used in real-time environments. Integrating additional data sources (e.g., radar and geostationary satellites) is an effective solution for improving the quality of the estimate, but it needs to cope with Big Data issues. To overcome all these issues, we propose a Rainfall Estimation Model (REM) based on an Ensemble of Deep Neural Networks (DeepEns-REM) that can automatically fuse heterogeneous data sources. The usage of Residual Blocks in the base models and the adoption of a Snapshot procedure to build the ensemble guarantees a fast convergence and scalability. Experimental results, conducted on a real dataset concerning a southern region in Italy, demonstrate the quality of the proposal in comparison with the Kriging interpolation technique and other machine learning techniques, especially in the case of exceptional rainfall events.

Extreme Rainfall Pattern Analysis for Drought Prone Shali Reservoir Area in West Bengal of India

Rainfall is one of the major sources of needed water. Rainfall amount also determines the agricultural and industrial activities in the region. The present study has been conducted to examine the rainfall trend in the Shali reservoir area of the Gangajalghati block in Bankura district in West Bengal. The Shali reservoir was made to provide irrigation water in the Gangajalghati block. Water insufficiency is a habitual threat to the people of this area. Here the planting and production of crops highly depend on the constricted period of inconsistent rainfall. About 80-90 % of rainfall usually occurs in this area from June to September until the onset of heavy rains. Therefore, in this drought-prone area, the analysis of rain patterns is very vital to understand the change in rainfall at all times. The monthly and annual rainfall data have been taken from field surveys and secondary surveys for almost the last 40 years (1980 to 2020). The Mann Kendall (MK) and Sen’s Slope estimate (SSE) tests have been applied to recognize the standing trend and the SSE test has been used to discover the existing trend and extent of change in rainfall over time. The rainfall data has been taken from ten rain gauge stations surrounding the study area. The relevant maps were prepared using Remote Sensing and the rainfall distribution pattern was analyzed with the help of the kriging interpolation technique of GIS. The annual MK test shows a positive trend but the season-wise MK test is showing a negative trend during monsoon season. SPI result describes that the study area is not getting normal rainfall during the monsoon season, it may create harmful effects on agricultural productivity. The study suggests taking proper drought management programs to combat this situation.

Geospatial analysis of groundwater quality using GIS: A case study of Ahafo Kenyasi, Ghana

The exploitation and sustainable use of groundwater has received much attention with the sudden decline in quantity and quality of surface water. Knowledge on the current status of the physico-chemical parameters of groundwater becomes important in ensuring the sustainable use of the resource. This study used Geographic Information System (GIS) to assess groundwater quality in Ahafo-Kenyasi with particular focus on determining the spatial distribution of groundwater quality parameters and also produce groundwater quality map of the area. Physico-chemical analyses of groundwater quality parameters were made after collection of water samples from 24 community boreholes. The results of analysis carried out showed the following concentration ranges: pH (5.12-6.54), EC (71.6-952μS/cm), TDS (35.08-465.59mg/l), Turbidity (0-6.25NTU), Ammonia (0.01-0.61mg/l), Nitrate (0.1-4.12mg/l), Sulphate (1-65.5mg/l). All the samples analysed were above the guidelines set by World Health Organization (WHO, 2011) except for pH and Turbidity. Spatial distribution maps of the individual water quality parameters were developed using kriging interpolation technique and accepted based on the prediction performances of Stable, Exponential, K-Bessel semivariogram models. Overall water quality of the study area was assessed using Water Quality Index (WQI). The results showed that groundwater quality in the area decreases from north-western to south-eastern. However, groundwater from Ahafo-Kenyasi is good for domestic purposes.

Integrated decision support for promoting crop rotation based sustainable agricultural management using geoinformatics and stochastic optimization

Sustainable agricultural management is essential for ensuring food security and economic development. Efficient agricultural land use based on crop rotation practices can deliver greater soil fertility and higher economic potential. We proposed a decision support tool (DST) for preserving land fertility, maximizing agricultural profit, minimizing agricultural pollution, and water usage. The proposed DST links geoinformatics, stochastic pairwise comparison (SPC), and constraint optimization to suggest the suitable crops for growing. To demonstrate the proposed DST, suitability of seven major crops in Muzaffarnagar district in Uttar Pradesh (India), where the footprint of sugarcane cultivable region is nearly 90% is analyzed and the findings are presented. The crops cultivated in the study region and the criteria suitable for their cultivation are identified using the hybrid system approach. The DST primarily encompasses qualitative and quantitative analysis coupled with geospatial analysis. Qualitative analysis guides the decision-maker in finalizing the crucial criteria to be assessed for cultivation, while quantitative analysis uses beta distribution for pairwise comparison to understand the significance of finalized criteria. We collected the data concerning parameters related to the finalized criteria by considering 2700 soil samples. Data required at the ungauged locations are estimated using the kriging interpolation technique. The findings of this study suggest that sugarcane can be allocated up to 20% of the land area. In addition to the principal crops (i.e., sugarcane, wheat, and rice), potato, mustard, maize, and sorghum also have good cultivation potential in Muzaffarnagar and can be grown on up to 20%, 22%, 18%, 21% of the land area respectively while just 1.5%, 1.8%, 0.1%, and 0% of land area, is used for their cultivation. With the prime focus on knowledge transfer from scientific studies to farmers, we used an open-source geospatial repository to develop an interactive dashboard that can fetch farmers' locations and present each crop's suitability based on optimized crop rotation practices.

Analysis of Groundwater Flow on Hydrogeological for Sustainable Development and Environment in the Ternate Basin, North Maluku Province, Indonesia

The sustainability of water and sanitation management is an important point in the 2021 SDGs program. To support the program, it is important to understand Indonesia's water cycle. Given the variety of characters, topography, and geology between islands in Indonesia can distinguish hydrogeological systems and their interactions with the surrounding environment. The design and management of water resources, especially groundwater, has an important role, considering the basin area is 106 km2, the character of a volcanic island with the name of an active volcano. Topography from 0-8% is a built-up zone, 8-14% is a cultivation zone, 14-45% is a protected forest, and >45% is a stratovolcano peak zone. Island morphology affects groundwater flow patterns. This study aimed to determine the characteristics of groundwater flow in the Ternate basin. Purposive sampling of 56 production wells, transmissivity value, conductivity, aquifer thickness, and topography was analyzed using the Cubic spline interpolation method. The results showed that the direction of flow and groundwater accumulation based on groundwater modeling using the kriging interpolation technique resulted in the interaction of groundwater flow and depression cones in 56 production wells due to the concentration and massive groundwater abstraction in the Ternate Basin. The decrease in groundwater level is fluctuating, from groundwater flows that occur radially-centrifugally, the total reserves of 56 production wells are 46 million m3/day, the average transmissivity value is 2.17 m2/day, production discharge is 51,710 m3/day.