Spatiotemporal Assessment Research Articles

Spatiotemporal assessment of the impact of olive-growing industry effluent on the Elmakhazine wadi dam in the Loukkos region (Morocco). Investigations into physicochemical, bacterial, and statistical processes

This study's goal is to assess the spatiotemporal effects of margined effluents from the Loukkos region's olive-growing industries during four time periods in 2020–2021 on the wadi Elmakhazine dam's surface waters. The effluents from eight sampling stations upstream and downstream of the dam were subjected to physico-chemical, bacteriological, and statistical analyses to meet this goal. A spatiotemporal monitoring of the physicochemical and bacteriological parameters of dam water pollution was also done. According to the findings of this study, these parameters follow an increasing gradient from the control source upstream to the dam's downstream and are characterized by high concentrations of chemical oxygen demand (COD), suspended matter (SSM), and biological oxygen demand (BOD5). Simultaneously, spatiotemporal monitoring of bacteriological parameters reveals a slight increase in total coliforms (TC), faecal coliforms (FC), and faecal streptococci (FS)from upstream to downstream. The statistical study using the principal component analysis (PCA) shows that the results obtained of the total information are very important for the whole periods of September-October (*), November-December (**), March-April (***), and July-August (****) such as these results have registered in the order of 99.91% according to axis F1 (92.76%) and F2 (7.15%). These results confirmed the spatiotemporal variation of the results obtained from the physicochemical and bacteriological parameters.

Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France

Current Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim was to forecast the land cover class of a group of pixels as a multi-class single-label classification problem given their time series gathered using satellite images. In this article, we exploit SITS to assess the capability of several spatial and temporal deep learning models with the proposed architecture. The models implemented are the bidirectional gated recurrent unit (GRU), temporal convolutional neural networks (TCNN), GRU + TCNN, attention on TCNN, and attention of GRU + TCNN. The proposed architecture integrates univariate, multivariate, and pixel coordinates for the Reunion Island’s landcover classification (LCC). the evaluation of the proposed architecture with deep neural networks on the test dataset determined that blending univariate and multivariate with a recurrent neural network and pixel coordinates achieved increased accuracy with higher F1 scores for each class label. The results suggest that the models also performed exceptionally well when executed in a partitioned manner for the LCC task compared to the temporal models. This study demonstrates that using deep learning approaches paired with spatiotemporal SITS data addresses the difficult task of cost-effectively classifying land cover, contributing to a sustainable environment.

Assessing spatiotemporal variation in drought characteristics and their dependence on timescales over Vidarbha Region, India

In this study, a detailed spatiotemporal assessment of meteorological drought characteristics (viz., persistence, severity, and frequency) is carried out for the Vidarbha Region of Maharashtra, India, which has been infamous for the increasing number of farmer suicides there in the recent past. Using the long-term rainfall data from 11 districts for 62 years (1951–2012), the droughts in different severity classes are characterized by SPI. A detailed investigation of SPI-3, SPI-6, and SPI-12 is also done to assess the dependence of the drought characteristics at different timescales. The results reveal that the Vidarbha region is prone to meteorological droughts with frequency from once in 5 to 8 years. However, the frequency and severity of droughts have significantly increased in 1982–2012 compared to 1951–1981. The persistence of droughts is well captured by SPI at longer timescales. The trend analysis reveals the drought severities to be increasing over 10 out of 11 districts.

Assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, India.

Drought is considered among the most perilous events with catastrophic consequences, particularly from the agro-economic point of view. These consequences are expected to exacerbate under the increasing meteorological aberrations due to changing climate, which necessitates investigating drought variabilities. This study presents a thorough spatiotemporal assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, Maharashtra, India. The precipitation data is extracted from the India Meteorological Department (IMD) gridded product, whereas actual evapotranspiration (ET) and Evaporative Stress Index (ESI) are obtained from Global Land Evaporation Amsterdam Model (GLEAM) datasets. Standardized Precipitation Index (SPI) is used to characterize drought occurrences at multiple time frames, whereas non-parametric tests, i.e., modified Mann-Kendall (MMK) and Sen's slope (SS) tests, are employed to detect trends. The results reveal the region to be prone to droughts, and SPI at a longer time frame (i.e., 12-monthly moving frame) can capture drought occurrences better than the shorter time frames, which can be attributed to the lesser randomness in the time series in the longer frame. A mix of positive/negative trends of SPI series are found for the monsoonal months; however, they are relatively more concentrated towards negative ZMMK. Hence, the Marathwada Region can be inferred to have exhibited a relatively increased tendency towards drought occurrences. The seasonal differences in mean values and trends of rainfall, ET, and ESI are discussed in detail. Since the Marathwada Region has a monsoon-dominated climate with high agricultural importance, the information reported in this study will help in devising water management strategies to minimize the repercussions of droughts.

Spatiotemporal assessment of groundwater quality in the Central Ganga Plain, India, using multivariate statistical tools.

In the present study, the water quality status of the Sadar block in Pratapgarh district of Uttar Pradesh, India, was calculated by Water Pollution Index (WPI) using 15 groundwater quality parameters (pH, turbidity, EC (electric conductivity), TDS (total dissolved solids), salinity, total hardness, Ca2+, K+, Mg2+, Na+, Cl-, F-, SO42-, HCO3-, and NO3-) at 40 different study locations. It is found that 10%, 25%, and 65%, of samples, fall under the good, moderately polluted, and highly polluted category, respectively. Irrigation suitability of groundwater has also been evaluated by using irrigation suitability parameters, i.e., Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Kelly Ratio (KR), Percentage Sodium (% Na), and Magnesium Hazards (MH). Based on the analysis, it was determined that most of the samples were suitable for irrigation in the area. Irrigation practices are found to be unsuitable for Kelly's ratio of 37.5%, MH 60%, and RSC 55% samples. WPI and Pearson's correlation matrix (PCA) have suggested a significant linear relationship among the water quality parameters based on the test results. The Gibbs plot indicates that the majority of samples fall under the "evaporation dominance" category while the rest fall under the "rock weathering dominance" suggesting that the complex geochemical mechanisms are active in the study area. This study would be very helpful to government authorities and it would aid them to suggest alternate drinking water resources in affected areas.

Aerobic glycolysis is important for zebrafish larval wound closure and tail regeneration.

The underlying mechanisms of appendage regeneration remain largely unknown and uncovering these mechanisms in capable organisms has far-reaching implications for potential treatments in humans. Recent studies implicate a requirement for metabolic reprogramming reminiscent of the Warburg effect during successful appendage and organ regeneration. As changes are thus predicted to be highly dynamic, methods permitting direct, real-time visualisation of metabolites at the tissue and organismal level would offer a significant advance in defining the influence of metabolism on regeneration and healing. We sought to examine whether glycolytic activity was altered during larval fin regeneration, utilising the genetically encoded biosensor, Laconic, enabling the spatiotemporal assessment of lactate levels in living zebrafish. We present evidence for a rapid increase in lactate levels within min following injury, with a role of aerobic glycolysis in actomyosin contraction and wound closure. We also find a second wave of lactate production, associated with overall larval tail regeneration. Chemical inhibition of glycolysis attenuates both the contraction of the wound and regrowth of tissue following tail amputation, suggesting aerobic glycolysis is necessary at two distinct stages of regeneration.

Spatiotemporal assessment of water security in the Dongting Lake region: Insights from projection pursuit method and sparrow search algorithm

The Dongting Lake region (TDLR) is China's essential commodity grain base and inland wetland reserve. Few studies, however, focus on the water security of TDLR. Additionally, as the attributes of water security increase, common methods may not obtain more structural characteristics or make full use of the data, resulting in the evaluation not being comprehensive enough. Hence, based on the vulnerability scoping diagram (VSD) and the coupled model combining projection pursuit method (PP), sparrow search algorithm (SSA), and contribution degree model, this study assesses the water security of TDLR and its three major cities. The result shows that the coupled model may be suitable to assess water security since it obtains a more accurate, comparable, sensitive, comprehensive evaluation more rapidly, intuitively, and stably. The water security of TDLR and three cities all exhibit an overall fluctuating upward trend during 2011–2020, with a significant increase in the comprehensive water security evaluation value (WSV) of TDLR (p < 0.05). Among the three cities, the water security of Yiyang is the safest, followed by Changde and Yueyang. A summary of the main contributing dimensions and top ten contributing indicators of water security of TDLR illustrates that the effective measures to improve its water security include building embankments and sewage treatment plants through increased investment; reducing industrial water consumption; improving the ability to convert rainwater to water resources and surface water storage capacity. Additional measures, such as strengthening the integrated management of the Dongting Lake and increasing the use of reclaimed water for ecological replenishment, can effectively improve its water security. This study objectively assesses and summarizes the water security of TDLR and provides a valuable reference for relevant authorities in regional water resources planning and management, as well as for water security assessments of other similar lake regions.

Citizen scientists’ dive computers resolve seasonal and interannual temperature variations in the Red Sea

Dive computers have the potential to provide depth resolved temperature data that is often lacking especially in close to shore, but spatiotemporal assessment of the robustness of this citizen science approach has not been done. In this study, we provide this assessment for the Red Sea, one of the most dived areas in the world. A comparison was conducted between 17 years of minimum water temperatures collected from SCUBA dive computers in the northern Red Sea (23–30° N, 32–39.4° E), satellite-derived sea surface temperatures from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) optimal interpolation product, and depth-banded monthly mean in-situ temperature from the TEMPERSEA dataset, which incorporates data originating from several in-situ recording platforms (including Argo floats, ships and gliders). We show that dive computer temperature data clearly resolve seasonal patterns, which are in good agreement in both phase and amplitude with OSTIA and TEMPERSEA. On average, dive computer temperatures had an overall negative bias of (–0.5 ± 1.1) °C compared with OSTIA and (–0.2 ± 1.4) °C compared with TEMPERSEA. As may be expected, increased depth-related biases were found to be associated with stratified periods and shallower mixed layer depths, i.e., stronger vertical temperature gradients. A south-north temperature gradient consistent with values reported in the literature was also identifiable. Bias remains consistent even when subsampling just 1% of the total 9310 dive computer datapoints. We conclude that dive computers offer potential as an alternative source of depth-resolved temperatures to complement existing in situ and satellite SST data sources.

Spatio-Temporal Assessment of Land use / Land cover using Normalized Difference Vegetation Index (NDVI) of Satellite Imageries and its relationship with Landslide-prone Zones in Kodaikanal Hill Station of Tamil Nadu, India

The land use exercises at last influence the land cover briefly as well as spatially. The primary consideration liable for the adjustment in land cover is to satisfy the developing requests of expanding populace through horticultural increase for nourishment and clearing of typical land covers like the forests and sparsely inhabited regions for settlement and business activities. The adjustment in land cover additionally upsets the other characteristic parts like soil ripeness, soil disintegration, environment, biodiversity, air quality and water system of the upset area. Remote sensing and GIS have been deployed to examine the changes in land use and land cover of the zone at spatial and transient scales. In the present study, the NDVI-based grouping explored a significant change in land use- land cover between 2009 and 2016. A significant change has been found in the forest cover area where around (3.34%) of the forest was occupied between 2009 and 2016. A comparison between the land use/land cover and landslide hazard zones was carried out which indicated the excessive anthropogenic activities (of construction and mechanized agriculture) as identified by built-up land and crop-land by NDVI studies need to be monitored and planned to maintain the safety, stability and popularity of this precious and geo-climatically strategic region.

Spatiotemporal assessment of land surface temperature and vegetation in tropical urban areas

Spatiotemporal assessment of land surface temperature and vegetation in tropical urban areas

Spatio-Temporal Assessment of Satellite Estimates and Gauge-Based Rainfall Products in Northern Part of Egypt

Egypt’s climate is generally dry all over the country except for the Northern Mediterranean Coast. The Egyptian Meteorological Authority (EMA) uses few meteorological stations to monitor weather events in the entire country within the area of one million square kilometers, which makes it scarce with respect to spatial distribution. The EMA data are relatively expensive to obtain. Open access rainfall products (RP) are commonly used to monitor rainfall as good alternatives, especially for data-scarce countries such as Egypt. This paper aims to evaluate the performance of 12 open access rainfall products for 8 locations in the northern part of Egypt, in order to map the rainfall spatial distribution over the northern part of Egypt based on the best RP. The evaluation process is conducted for the period 2000–2018 for seven locations (Marsa-Matrouh, Abu-Qeir, Rasheed, Port-Said, Tanta, Mansoura, and Cairo-Airport), while it is conducted for the period 1996–2008 for the Damanhour location. The selected open access rainfall products are compared with the ground stations data using annual and monthly timescales. The point-to-pixel approach is applied using four statistical indices (Pearson correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and bias ratio (Pbias)). Overall, the results indicate that both the African Rainfall Estimation Algorithm (RFE) product and the Climate Prediction Center (CPC) product could be the best rainfall data sources for the Marsa-Matrouh location, with relatively higher r (0.99–0.93 for RFE and 0.99–0.89 for CPC) and NSE (0.98–0.79 for RFE and 0.98–0.75 for CPC), in addition to lower RMSE (0.94–7.78 for RFE and 0.92–12.01 for CPC) and Pbias (0.01–11.95% for RFE and −2.22–−12.15% for CPC) for annual and monthly timescales. In addition, the Global Precipitation Climatology Centre (GPCC) and CPC give the best rainfall products for the Abu-Qier and Port-Said locations. GPCC is more suitable for the Rasheed location. The most appropriate rainfall product for the Tanta location is CHIRPS. The current research confirms the benefits of using rainfall products after conducting the recommended performance assessment for each location.

Crime patterns in Delhi: a Bayesian spatio-temporal assessment

Crime patterns in Delhi: a Bayesian spatio-temporal assessment

Drought characterization over Indian sub-continent using GRACE-based indices

Drought is a natural disaster affects water resources, agriculture, and social and economic development due to its long-term and frequent occurrence. It is crucial to characterize and monitor drought and its propagation to minimize the impact. However, spatiotemporal assessment of drought characteristics over India at the sub-basin scale based on terrestrial water storage is unexplored. In this study, the Terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment and precipitation data are used to characterize the propagation of drought. Combined Climatological Deviation Index (CCDI) and GRACE-Drought Severity Index (GRACE-DSI) were computed as CCDI utilizes both precipitation and TWSA data while GRACE-DSI uses only TWSA data. Our results showed that GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to depletion of TWS. While other sub-basins show changing trends for GRACE-DSI and CCDI. The number of sub-basins showing significant negative trends for GRACE-DSI is more than that for CCDI. Hence TWS is depleting for most of the subbasins in India. Our results show that Indo-Gangetic plains face many drought events during 2002–2004, 2009–2014 & 2015–2017. Maximum drought duration and drought severity obtained for the area of North Ladakh (not draining into Indus basins) by GRACE-DSI are 26 months (2002–2004) and − 44.2835, respectively. The maximum drought duration and drought severity obtained for the Shyok sub-basin by CCDI is 17 months (2013–2015) and − 13.4392, respectively. Monthly trend analysis revealed that 39 & 23 no. of sub-basins show significant negative GRACE-DSI trends for October and CCDI for November, respectively. At the same time, the seasonal trend shows that total 34 and 14 sub-basins exhibited a significant negative trend at post-monsoon Kharif season for both the GRACE-DSI & CCDI, respectively.

The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow.

The decline of pollinating insects in agricultural landscapes proceeds due to intensive land use and the associated loss of habitat and food sources. The feeding of those insects depends on the spatial and temporal distribution of nectar and pollen as food resource. Hence, to protect insect biodiversity, a spatio-temporal assessment of food quantity of their habitats is necessary. Therefore, sufficient data on traits of floral resources are required. As floral resources' traits of plants are important to quantify food availability, we present two databases, the FloRes Database (Floral Resources Database) and the raw database, from where FloRes was derived. Both databases contain the plant traits: (1) flowering period, (2) floral-unit density per day, (3) nectar volume per floral unit per day, (4) sugar content per floral unit, (5) sugar concentration in nectar, (6) pollen mass or volume per floral unit and per day, (7) protein content of pollen and (8) corolla depth. All traits were sampled from literature and online databases. The raw database consists of 702 specified plant species, 138 unspecified species 37 species (spec., sp), 22 species pluralis (spp) and, for 79, only the genus was identified) and two species complexes (agg.). Those 842 taxa belong to 488 genera and 102 families. Finally, only 27 taxa have a complete set of traits, too few for a sufficient assessment of spatio-temporal availability of floral food-resources.As information on floral resources is scattered throughout many publications with different units, we also present our multistep workflow implemented in five consecutive R-scripts. The multistep workflow standardises the trait units of the raw database to comparable entities with identical units and aggregates them on a reasonable taxonomic level into the second application database, the FloRes Database. Finally, the FloRes Database contains aggregated information of traits for 42 taxa and, when corolla depth is excluded, for 72 taxa.This is the first attempt to gather these eight traits from different literature sources into one database with a multistep workflow. The publication of the multistep workflow enables the users to extend the FloRes Database on their own demands with other literature data or newly-gathered data to improve quantification of food resources. Especially, the combination of pollen, nectar and the open flowers per square metre is, as far as we know, a novelty.The FloRes Database can be used to evaluate the quantity of food-resource habitats available for pollinators, for example, to compare seed mixtures of agri-environmental measures, such as flower strips, considering flower phenology on a daily basis.

Spatio-temporal assessment of changing land surface temperature and depleting water in the Lake Chad area

Lake Chad is located at the south of the Sahara Desert in an arid region. The lake’s water resources are under severe pressure due to the basic needs of the growing population around the lake, global warming, and increasing irrigation demands. Numerous land cover change studies have measured the rate of depletion of the lake’s surface water. However, the contribution of the increasing high temperatures in the region which is also a compounding factor has received little attention. In this study, an assessment of the changes in surface water extent of Lake Chad from 1973-2017 was carried out through a land cover analysis. The potential influence of the rising land surface temperatures on the water losses was also studied. The extraction of the land cover was done using maximum likelihood classification. The results show that between 1973 and 1987, the lake lost 12,796.81km2 of its surface water area. This period coincided with a season of drought and dry seasons reported to have occurred in the lake’s area during the 1970s. Between 1987 and 2003, average temperature rise and change in surface water area was +1.54˚C and +962.71km2 respectively. Between 2003 and 2017, average temperature rise and change in surface water area was +3.69˚C and -25.17km2 respectively. These results provide further evidence of the alarming rate of water loss in the lake’s environment, and suggest a link between rising land surface temperatures and diminution of the lake’s water. The findings inform efforts directed at addressing the ecological problem facing the lake.

Spatio-temporal rainfall variability and its linkage with large scale climate oscillations over the Republic of South Sudan

Climate change has distorted the rainfall spatio-temporal distribution. Therefore, understanding the spatio-temporal rainfall variation and its causes at meso and micro-level could provide insight for the water and agricultural development interventions. This article examines the spatio-temporal rainfall variability and driving factors in connection with large scale global oscillation. CHIRPS rainfall and NCEP/NCAR wind fields data from 1981 to 2019 were used. Rotaed Emperical Ortogonal Function (REOF) was employed to investigate spatio-temporal rainfall variability. Large scale moisture transport was studied using lower (1000–850 hPa), middle (600–500 hpa), and upper (300–100 hPa) level wind fields. The result reveals that in South Sudan rainfall dominant variance regions were not uniform across seasons. Northern, Southcentral, and Southeastern tip regions were the top three dominant variance regions which explain around 30 %, 25 % and 17 % of the total variation in REOF1, REOF2, and REOF3 during the spring season respectively. Likewise, during the summer season the dominant variance REOF1 (28 %), REOF2 (24 %), and REOF3 (9 %) was observed in South-east, West, and, North-west, whereas; the autumn dominant variance in North-west, North-east, and South-east explain about REOF1 (22.6 %), REOF2 (19.9 %), and REOF3 (16.7 %) of the total variation respectively. The alternative wet and dry years were causing drought and flooding in the study sites. Low-level southeasterly winds from India and over the equator from the Atlantic Ocean carry rain-bearing moisture to South Sudan. The winds converge and convection is triggered by African Easterly and Tropical Easterly jet favors convection during the summer and spring seasons. Sea Surface temperature shows a positive and negative correlation with rainfall variability. Rainfall spatio-temporal assessment is crucial for water resource management, agricultural productivity, and climate change mitigation.

A spatiotemporal assessment of the high-resolution CHIRPS rainfall dataset in southwestern Colombia using combined principal component analysis

Long and temporal time-consistency rainfall time series are essential for studying climate; nevertheless, raingauge stations are unevenly distributed across southwestern Colombia. This research paper assesses the consistency of the satellite rainfall estimate from Climate Hazards Group Infrared Precipitation (CHIRPS) via pixel-to-point comparison with 46 observed monthly rainfall time series using four pairwise metrics and Combined Principal Component Analysis (CPCA). Two Combined Principal Components (CPC) were also used to determine the relationship with the Sea Surface Temperature (SST) through simultaneous linear correlation maps. The results showed that CHIRPS has a better performance in the Andean region than in the Pacific region. The correlation between CPC1 (CPC2) and SST showed a typical El Niño Southern Oscillation pattern with an inverse (direct) relationship between the rainfall in the Andean (Pacific) Region. Finally, our results validate that CHIRPS can be included in further studies of the spatiotemporal dynamics of rainfall in Southwestern Colombia.

Spatio-temporal assessment of whole-body center of mass coordination on standard maximum vertical jump

Spatio-temporal assessment of whole-body center of mass coordination on standard maximum vertical jump

Spatio-temporal assessment of rainfall erosivity in Ecuador based on RUSLE using satellite-based high frequency GPM-IMERG precipitation data

Soil erosion caused by rainfall is one of the main soil degradation problems in Ecuador and has major implications on conservation and management of water and soil resources, disaster control, agricultural protection and other applications in the Ecuadorian territory. Extensive precipitation time series are required to estimate the rainfall erosivity as a triggering element of soil erosion, however, meteorological stations distribution in Ecuador is not homogeneous over the national territory (greater concentration in coastal basins) and present high lack of data, generating an incorrect rainfall distribution that requires resorting to other types of available information. The objective of this work was to analysis the spatio-temporal distribution of potential soil erosion through rainfall erosivity in Ecuador, through an innovative method based on the Revised Universal Soil Loss Equation (RUSLE) employing GPM-IMERG rainfall data. Our methodology considered the delimitation and evaluation of the watersheds environmental characteristics, GPM-IMERG, GPCC and ERG-data evaluation as providers of rainfall databases, and calculation of the erosive force of the rain (R-Factor) over the period 2001–2020. Basins delimitation and layers of environmental characteristics were obtained from regional literature; GPM-IMERG and GPCC from web servers; ERG-data from INAMHI-Ecuador; R-Factor (30 min) calculation was made from GPM-IMERG parameters and its was validated with RIST software using a representative subset of pixels, revealing an excellent compatibility of almost 1% average difference. New information was obtained from spatial, interannual and seasonal R-Factor distribution considering environmental characteristics, even though the satellite image products (GPM-IMERG) still underestimate the high values of R-Factor compared to Literature. New models for Ecuador have been established to estimate the R-Factor from annual rainfall, which allow it to be calculated since the beginning of the last century as well as generate projections of future soil erosion. This helps territory managers to take new measures to deal with soil loss.

A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990–2030

Analyzing long term urban growth trends can provide valuable insights into a city’s future growth. This study employs LANDSAT satellite images from 1990, 2000, 2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth. Land Use Land Change (LULC) maps developed using the Maximum Likelihood classifier produce four principal classes: Built-up, Vegetation, Water body, and “Others”. In between 1990–2019, the total built-up area expanded by 130%, 132 km2 in 1990 to 305 km2 in 2019. Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km2 of additional built-up area to accommodate every 100,000 new residents. Further, a Multi-Layer Perceptron — Markov Chain model (MLP-MC) predicts Ahmedabad’s urban expansion by 2030. Compared to 2019, the MLP-MC model predicts a 25% and 19% increase in Ahmed-abad’s total urban area and population by 2030. Unaltered, these trends shall generate many socio-economic and environmental problems. Thus, future urban development policies must balance further development and environmental damage.