Study on the Influence of Salinity, TDS, GWL and Land Use in the Coastal Region of Kebumen Regency with a Statistical Approach

This study investigates the impact of salinity levels, total dissolved solids (TDS), groundwater levels (GWL), and land use patterns in the coastal region of Kebumen Regency, employing a statistical approach. Coastal areas are often vulnerable to salinity variations, which can significantly affect the surrounding ecosystems and land utilization. The research aims to discern the correlation between TDS, salinity levels, GWL and land use practices in this specific region, employing statistical analysis as a means of quantifying and understanding these relationships. Total dissolved solids (TDS) represent the concentration of inorganic salts, minerals, and ions in water bodies and play a crucial role in the overall water quality of coastal areas. Salinity levels, on the other hand, directly affect TDS and are influenced by factors such as tidal patterns and freshwater input. Groundwater levels affect salinity and TDS. Land use practices, including agricultural, residential, and industrial activities, profoundly impact coastal ecosystems and are often influenced by salinity levels. This study utilizes statistical methods to analyze the complex interplay between TDS, salinity, GWL, and land use in the Kebumen Regency coastal region. By examining data related to TDS, salinity, GWL, and land utilization, this research seeks to contribute valuable insights into the sustainable management of coastal environments. The findings of this study are expected to inform decision-making processes for the benefit of local communities and ecosystems, guiding future policies and practices for the preservation and balanced development of coastal areas in Kebumen Regency


Introduction
Coastal regions are critical interfaces between terrestrial and marine environments, supporting diverse ecosystems and providing vital resources for human communities.The coastal region of Kebumen Regency is a unique and dynamic area that is influenced by various factors such as total dissolved solids (TDS), salinity levels, groundwater levels (GWL), and land use.The coastal region of Kebumen Regency, with its unique geographical features, is a microcosm of the delicate balance between natural processes and anthropogenic activities.Understanding the intricate dynamics of this region is crucial for sustainable development and resource management.
Total Dissolved Solids (TDS) in scientific terms refer to the cumulative concentration of inorganic and organic substances that are present in a solution in a molecular, ionized, or colloidal form.TDS is typically expressed in milligrams per liter (mg/L) or parts per million (ppm) and is commonly measured in water quality analysis.These dissolved substances can include various ions, such as 1339 (2024) 012008 IOP Publishing doi:10.1088/1755-1315/1339/1/012008 2 calcium, magnesium, sodium, potassium, sulfates, chlorides, carbonates, as well as organic compounds, and trace elements.TDS is an important parameter in environmental and water quality assessments because it provides an indication of the overall solute content in a solution.It can originate from various sources, including natural geological processes, industrial discharges, agricultural runoff, and human activities.
TDS and salinity are interrelated.This is due to the increase in salinity content, which also leads to an increase in TDS content.Coastal regions tend to have the potential for seawater intrusion.The definition of seawater intrusion is the infiltration or entry of seawater into the pores of rock and/or soil, thereby contaminating the groundwater conditions contained within it.Seawater intrusion occurs when seawater enters the empty rock pores as a result of extensive groundwater usage [1].
Salinity intrusion, caused by factors such as sea level rise and land subsidence, also seawater intrusion and precipitation, poses significant challenges to agriculture in coastal areas [2].Land use also has a significant impact on groundwater resources, affecting recharge and water demand [3].Salinity intrusion also plays a pivotal role in shaping the environmental landscape.One of the key challenges faced by coastal areas worldwide is the influence of salinity variations on both natural ecosystems and human activities.In Kebumen Regency, where coastal communities are intricately connected to the surrounding ecosystems, the impact of salinity on soil and water quality can have profound implications for agriculture, biodiversity, and overall ecosystem health.
Groundwater level (GWL) is the depth at which the water table or the upper surface of the saturated zone is located below the ground surface.It is a critical parameter in hydrogeology and is of great importance for understanding groundwater dynamics, aquifer behavior, and water resource management.Groundwater level is typically measured in meters or feet below the ground surface.It fluctuates over time in response to various factors, including recharge (infiltration of water into the aquifer), discharge (withdrawal of water from the aquifer), and variations in precipitation.Changes in groundwater levels can have significant implications for the availability and quality of groundwater resources.
In addition to salinity, land use plays a vital role in shaping the coastal region of Kebumen Regency.The distribution of land use, particularly in the agriculture, forestry, and fishery sectors, has a significant impact on the region's economic development [4].Assessing the carrying capacity of agricultural land in disaster-prone areas is essential for effective land management and sustainable development [4].Furthermore, changes in land use, such as paddy land abandonment, can be linked to long-term soil salinity changes [5].Understanding the relationship between land use and soil salinity is crucial for effective land management and mitigating the negative impacts of salinity on agricultural productivity in the future.
This article presents a comprehensive study focused on unraveling the complex interplay between salinity, TDS, GWL and land use in the coastal region of Kebumen Regency.Employing a statistical approach, we aim to discern patterns, trends, and correlations within the data, shedding light on the factors influencing salinity levels and their subsequent effects on land use patterns.The significance of this study lies in its potential to inform evidence-based decision-making for sustainable coastal management and community resilience.By bridging the gap between scientific inquiry and practical applications, our research strives to contribute valuable insights for policymakers, researchers, and local stakeholders vested in the well-being of the coastal communities in Kebumen Regency.
As we delve into the intricacies of salinity dynamics and land use practices, our endeavor is to foster a holistic understanding of the coastal environment.Through rigorous statistical analysis, this study seeks not only to identify current challenges but also to pave the way for proactive measures that can mitigate the impact of salinity variations and promote the sustainable coexistence of human activities and natural ecosystems in this ecologically sensitive region.

Research Area
The research is located in the coastal area of Mirit and Ambal District, southern side of Kebumen regency, Central Java (see Figure 1).This research was conducted through three stages of research.The first stage is the preparatory stage through library research or literature studies, by collecting theoretical data from journals, reference books, and guides related to regional conditions in the research area.The second phase involved a field survey technique, where data gathering occurred through the direct measurement of wells belonging to residents at 95 specified locations.A grid distance, approximately 500 meters separated each well.The measurements included determining the groundwater table depth (utilizing a tape measure), as well as assessing temperature, pH, electrical conductivity (EC), and total dissolved solids (TDS) using a handheld pH-TDS-EC meter.The third stage is determining variables data analytics, linear regression, spatially using GIS, converting spatial variables into numbers, and then weighting these variables, which will then be processed statistically using python programming language and ArcGIS software to determine the influence of salinity, TDS, GWL, and land use in the coastal region of Kebumen regency.The data analysis is conducted with the aim of seeking a linear regression relationship between TDS and salinity.

Data Collection and Processing
This research will utilize predetermined variables used salinity, TDS, GWL, and land use in the coastal region of Kebumen regency.Direct field survey and inspection are employed to gather data on TDS, land use and soil texture.Several field samples were subjected to water geochemistry testing in the laboratory resulting in salinity data.Concurrently, land use determination within the area is also elaborated with GIS, utilizing the latest Google Earth imagery and dataset from Indonesian Geospatial Information Agency (BIG), employing a digitalization process.Lithology data is also elaborated on the regional geological map of Kebumen [6].Alluvial deposits in the northern part of the research area (Qa) generally consist of well-sorted loose sand.Meanwhile, beach deposits (Qac) in the southern part consist of gravel, clay, silt, and sand.The selection of data aligns with the specific requirements of variables in this study, with on-site inspections conducted in the research area.Table 1 provides details on each required variable and its respective data sources.The field data obtained are subsequently processed using statistical methods and various supporting tools.Some of the field data undergo geochemical testing in the laboratory.The initial processing involves conducting basic statistical analysis of the laboratory data in order to establish a linear relationship between Total Dissolved Solids (TDS) and salinity.This linear relationship is determined using the Python programming language with concepts of basic statistics, linear regression, and analytical data heatmaps.
The second stage of processing involves assigning weights to the variables used, such as TDS, groundwater level (GWL), and land use.Subsequent processing utilizes the ArcGIS application to model Inverse Distance Weighting (IDW) and contour the spatial distribution of data from each well using the aforementioned variables: TDS, GWL, and land use.The final stage of processing involves combining the weighting from each map produced, resulting in a map of the influence of TDS, GWL, and land use.

Research Variables
This study will utilize the statistical approach and GIS model as its foundation, necessitating the spatial conversion of variables.In Table 2, three variables-namely, land use, total dissolved solids (TDS), and groundwater levels (GWL), -will be employed.All these variables will be transformed into spatial data, particularly for the identification of recharge and discharge areas influencing the salinity rate in research area, as outlined by [7].For ease of GIS software analysis, the complete set of variables will be converted into either thematic maps or spatial data.Simultaneously, the weight assigned to each variable is determined through alignment with its role in influencing the salinity rate in research area.Subsequently, scoring values are calibrated according to the classification of each variable, ranging from a score of 5 denoting the highest value to a score of 1 indicating the lowest.This categorization is defined based on the classification of each variable, elucidating its capacity for infiltration and surface water percolation [8], except for classification of total dissolve solids (TDS) [9].The considerable chemical diversity in water contributes to its elevated TDS value, leading to heightened salinity and electrical conductivity.Furthermore, the weights of each variable are established considering their impact on the ability of surface water infiltration into the ground.

Statistic Descriptive
The following are laboratory data (Table 3) that has been collected, resulting in water chemistry data that is subsequently presented in descriptive statistics (Table 4).This research use python programming to generate descriptive statistics.

Regression Linear
This research utilizes the Python programming language to generate heatmaps (Figure 2) and visualize the machine learning of the linear regression relationship between salinity and TDS (Figure 3).The figure illustrates a relationship between salinity and TDS with a correlation coefficient of 0.98, indicating a strong linear association approaching a positive one.In a linear relationship between salinity and TDS, we can assume that as salinity increases, TDS values will also increase.This assumption allows salinity to be represented by TDS values.

Influence TDS, GWL, and Land Use Map
The salinity data, which exhibits a linear relationship with TDS, can be represented by TDS data due to limitations in laboratory data.The TDS data, groundwater level (GWL), and land use data are subsequently weighted using the Inverse Distance Weighting (IDW) statistical model and overlay weighting using ArcGIS, then resulting in the creation of a map (Figure 4).

Conclusion
From some of the explanations above, it can be concluded that several points: Relationship between salinity and TDS with a correlation coefficient of 0.98, indicating a strong positive linear association.The southern regions in proximity to the sea exhibit higher values of salinity, TDS (Total Dissolved Solids), groundwater level (GWL), and land use influence.Conversely, the northern areas display relatively lower values of salinity, TDS, GWL, and land use influence due to their greater distance from the coastline.

Figure 1 .
Figure 1.Location of research area.

Figure 4 .
Figure 4. Influence of TDS, GWL, and Land Use Map in The Coastal Region of Kebumen Regency.

Table 1 .
Data and Source.

Table 2 .
Variables, criteria, classification and determination of salinity rate influencer, based on identification of recharge and discharge areas.

Table 3 .
Water Chemistry Laboratory Data.

Table 4 .
Statistic Descriptive Water Chemistry Laboratory Data.